NIST Special Publication 500-209

Computer

Systems

Technology

U.S. DEPARTMENT OF

COMMERCE

Technology Administration

National Institute of Standards and

Technology

Software Error Analysis

Wendy W. Peng

Dolores R. Wallace

NAT L INST. OF ST4ND & TECH R I.C.

A111D3 TTSTll

NISI

PUBLICATIONS

^QC

' 100

.U57

//500-209

1993
7he National Institute of Standards and Technology was established in 1988 by Congress to "assist industry in the development of technology . . . needed to improve product quality, to modernize

manufacturing processes, to ensure product reliability . . . and to facilitate rapid commercialization . . , of

products based on new scientific discoveries."

NIST, originally founded as the National Bureau of Standards in 1901, works to strengthen U.S.

industry's competitiveness; advance science and engineering; and improve public health, safety, and the

environment. One of the agency's basic functions is to develop, maintain, and retain custody of the national

standards of measurement, and provide the means and methods for comparing standards used in science,

engineering, manufacturing, commerce, industry, and education with the standards adopted or recognized

by the Federal Government.

As an agency of the U.S. Commerce Department's Technology Administration, NIST conducts basic

and applied research in the physical sciences and engineering and performs related services. The Institute does generic and precompetitive work on new and advanced technologies. NIST's research facilities are located at Gaithersburg, MD 20899, and at Boulder, CO 80303. Major technical operating units and their principal activities are listed below. For more information contact the Public Inquiries Desk, 301-975-3058.

Technology Services

• Manufacturing Technology Centers Program

• Standards Services

• Technology Commercialization

• Measurement Services

• Technology Evaluation and Assessment

• Information Services

Electronics and Electrical Engineering

Laboratory

• Microelectronics

• Law Enforcement Standards

• Electricity

• Semiconductor Electronics

• Electromagnetic Fields'

• Electromagnetic Technology'

Chemical Science and Technology

Laboratory

• Biotechnology

• Chemical Engineering'

• Chemical Kinetics and Thermodynamics

• Inorganic Analytical Research

• Organic Analytical Research

• Process Measurements

• Surface and Microanalysis Science

• Thermophysics^

Physics Laboratory

• Electron and Optical Physics

• Atomic Physics

• Molecular Physics

• Radiometric Physics

• Quantum Metrology

• Ionizing Radiation

• Time and Frequency'

• Quantum Physics'

Manufacturing Engineering Laboratory

• Precision Engineering

• Automated Production Technology

• Robot Systems

• Factory Automation

• Fabrication Technology

Materials Science and Engineering

Laboratory

• Intelligent Processing of Materials

• Ceramics

• Materials Reliability'

• Polymers

• Metallurgy

• Reactor Radiation

Building and Fire Research Laboratory

• Structures

• Building Materials

• Building Environment

• Fire Science and Engineering

• Fire Measurement and Research

Computer Systems Laboratory

• Information Systems Engineering

• Systems and Software Technology

• Computer Security

• Systems and Network Architecture

• Advanced Systems

Computing and Applied Mathematics

Laboratory

• Applied and Computational Mathematics^

• Statistical Engineering^

• Scientific Computing Environments^

• Computer Services^

• Computer Systems and Communications^

• Information Systems

'At Boulder, CO 80303. ^Some elements at Boulder, CO 80303.
NIST Special Publication 500-209

Software Error Analysis

Wendy W. Peng

Dolores R. Wallace

Systems and Software Technology Division

Computer Systems Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899

April 1993

U.S. DEPARTMENT OF COMMERCE

Ronald H. Brown, Secretary

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Raymond G. Kammer, Acting Director
Reports on Computer Systems Technology

The National Institute of Standards and Technology (NIST) has a unique responsibility for computer

systems technology within the Federal government. NIST's Computer Systems Laboratory (CSL) develops standards and guidelines, provides technical assistance, and conducts research for computers and

related telecommunications systems to achieve more effective utilization of Federal information technology resources. CSL's responsibilities include development of technical, management, physical, and administrative standards and guidelines for the cost-effective security and privacy of sensitive unclassified

information processed in Federal computers. CSL assists agencies in developing security plans and in improving computer security awareness training. This Special Publication 500 series reports CSL re- search and guidelines to Federal agencies as well as to organizations in industry, government, and

academia.

National Institute of Standards and Technology Special Publication 500-209

Natl. Inst. Stand. Technol. Spec. Publ. 500-209, 113 pages (Apr. 1993)

CODEN: NSPUE2

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1993
ABSTRACT

This document provides guidance on software error analysis. Software error analysis includes

error detection, analysis, and resolution. Error detection techniques considered in the study are those used in software development, software quality assurance, and software verification,

validation and testing activities. These techniques are those frequendy cited in technical literature

and software engineering standards or those representing new approaches to support error

detection. The study includes stadstical process control techniques and rslates them to their use

as a software quality assurance technique for both product and process improvement. Finally,

the report describes several software reliability models.

KEYWORDS

Data Collection; Error Detection; Error Removal; High Integrity Software; Metrics; Software

Error Analysis; Software Quality Assurance; Software Verification and Validation; Stadsdcal

Process Control

iii

EXECUTIVE SUMMARY

The main purpose of this document is to provide the software engineering community with

current information regarding error analysis for software, which will assist them to do the

following:

• Understand how error analysis can aid in improving the software development process;

• Assess the quality of the software, with the aid of error detection techniques;

• Analyze errors for their cause and then fix the errors; and

• Provide guidelines for the evaluation of high-integrity software.

The software industry is currently still young, without sufficient knowledge and adequate

standards to guarantee fault-free software. Although research continues to identify better

processes for error prevention, with current practices, errors will probably occur during software

development and maintenance. Hence, there is the need for error analysis. Error analysis for

software includes the acdvities of detecting errors, collecdng and recording error data, analyzing

and removing single errors, and analyzing collective error data to remove classes of errors. The

collective error data may be used with stadsdcal process control (SPC) techniques to improve the

product and the processes used in developing, maintaining, and assuring the quality of software.

This report provides a descripdon of error detection techniques which are cited frequently in

technical literature and standards and describes the cost benefits of applying error detection early

in the lifecycle. However, error detection alone is not sufficient for removal of an error. Informadon must be recorded about the error to assist in the analysis of its cause, its removal,

and its relationship to the project and to other similar projects. This report provides guidance on data collecdon, analysis, and removal as well as error detecdon.

This report describes how several SPC techniques can be used for software quality assurance technique and for process improvement. The report identifies metrics related to software error detection and identifies several software reliability esdmation models. Metrics are used to assess

the product or process, while SPC techniques are used to monitor a project by observing trends.

SPC techniques help to locate major problems in the development process, the assurance

processes (e.g., software quality assurance, verification and validation), and the product itself

The study of software engineering standards reported in [NUREG, NIST204] indicates that

standards are beginning to associate requirements for error detection techniques with the quality

requirements and problem types of the software project implementing the standard. Further

examination of these documents and additional standards and guidelines for high integrity

software indicates that these documents vary widely in their recommendations of specific error

v
techniques. Appendix B provides a summary of the error detection techniques required or recommended by these documents for the assurance of the quality of high integrity software.

This report recommends collection of error data into an organizational database for use by a vendor' over several projects, and modified collections of these databases for use by government

auditors (e.g., Nuclear Regulatory Commission, Environmental Protection Agency). Software

organizations should consider institudonalizing mechanisms for establishing and maintaining a database of error analysis data within their organizadon. Over time, it may become apparent that

some error analysis techniques are more effecdve than others with respect to a given type of

problem. It may also become apparent that problems in these areas occur most often with certain

development pracdces and less frequently with others. The database must contain both

developmental and operational error data to be effective. In the regulators' environment, auditors

may use the information in the database to identify the most error-prone features of specific high

integrity systems and may ensure that their audits examine these features carefully. The auditors

may use the data to identify acceptance limits on different aspects of a high integrity system.

An organizational database may also play an important role in software reuse within an

organization. In deciding whether or not to reuse a particular software component, one can examine its error history to determine whether it satisfies the level of assurance required by the

intended application. One can evaluate the component by observing its past failure rates and

fault densities to ensure that the component is appropriate for reuse. A software component may

sometimes be reused to build a system which is of a higher level of assurance than that in which

the component was originally used. The database would provide data on the reliability or other

quality attributes to help determine how much additional work is needed to increase the quality

of the component to the desired level.

^In this report, the term "vendor" includes software developers.

vi
TABLE OF CONTENTS

1. OVERVIEW 1-1

1.1. Definitions 1-2

2. INTRODUCTION TO SOFTWARE ERROR ANALYSIS 2-1

2.1. Cost Benefits of Early Error Detection 2-2

2.2. Approach to Selecting Error Analysis Techniques 2-3

3. TECHNIQUES FOR DETECTING ERRORS 3-1

3.1. Classes of Error Detection Techniques 3-1

3.2. Techniques Used During the Lifecycle 3-2

3.2.1. Requirements 3-4

3.2.2. Design 3-5

3.2.3. Implementation 3-5

3.2.4. Test 3-7

3.2.5. Installation and Checkout 3-8

3.2.6. Operation and Maintenance 3-8

3.3. Benefits of Classes of Error Detection Techniques 3-8

4. REMOVAL OF ERRORS 4-1

4.1. Idendfication 4-1

4.2. Investigation 4-3

4.3. Resolution 4-4

4.3.1. Resolution Plan 4-4

4.3.2. Resolution Action 4-4

4.3.3. Corrective Action 4-5

4.3.4. Follow-up 4-5

4.4. Use of Individual Error Data 4-5

5. TECHNIQUES FOR THE COLLECTION AND ANALYSIS OF ERROR

DATA 5-1

5.1. Error History Profile / Database 5-1

5.2. Data Collection Process 5-2

5.3. Metrics 5-4

5.3.1. Metrics Throughout the Lifecycle 5-6

5.3.1.1. Metrics Used in All Phases 5-6

5.3.1.2. Requirements Metrics 5-8

5.3.1.3. Design Metrics 5-9

5.3.1.4. Implementation Metrics 5-11

5.3.1.5. Test Metrics 5-13

5.3.1.6. Installation and Checkout Metrics 5-16

vii
5.3.1.7. Operation and Maintenance Metrics 5-16

5.4. Statistical Process Control Techniques 5-17

5.4.1. Control Chart 5-18

5.4.2. Run Chart 5-21

5.4.3. Bar Graph 5-21

5.4.4. Pareto Diagram 5-23

5.4.4. Histogram 5-24

5.4.5. Scatter Diagram 5-26

5.4.6. Method of Least Squares (Regression Technique) 5-27

5.5. Software Reliability Estimation Models 5-28

6. SUMMARY 6-1

7. REFERENCES 7-1

APPENDIX A. ERROR DETECTION TECHNIQUES A-1

A.l. Algorithm Analysis A-1

A.2. Back-to-Back Testing A-1

A.3. Boundary Value Analysis A-2

A.4. Control Flow Analysis/Diagrams A-3

A. 5. Database Analysis A-3

A. 6. Data Flow Analysis A-4

A.7. Data Flow Diagrams A-4

A. 8. Decision Tables (Truth Tables) A-5

A.9. Desk Checking (Code Reading) A-6

A. 10. Error Seeding A-7

A.ll. Finite State Machines A-8

A. 12. Formal Methods (Formal Verification, Proof of Correctness, Formal Proof

of Program) A-8

A. 13. Information Flow Analysis A-9

A.M. (Fagan) Inspections A-9

A. 15. Interface Analysis A- 10

A. 16. Interface Testing A-11

A. 17. Mutation Analysis A- 12

A. 18. Performance Testing , A- 13

A. 19. Prototyping/ Animation A-13

A. 20. Regression Analysis and Testing A- 14

A.21. Requirements Parsing A- 14

A. 22. Reviews A-15

A. 23. Sensitivity Analysis A-15

A.24. Simulation A- 16

A. 25. Sizing and Timing Analysis A- 17

A.26. Slicing A-18

A. 27. Software Sneak Circuit Analysis .A-18

viii
A.28. Stress Testing A- 19

A. 29. Symbolic Execution A-20

A.30. Test Certification A-20

A.31. Traceability Analysis (Tracing) A-21

A.32. Walkthroughs A-22

APPENDIX B. ERROR ANALYSIS TECHNIQUES CITED IN SOFTWARE

STANDARDS A-23

TABLES

Table 3- la. Error Detection Techniques and Related Techniques (part 1) 3-2

Table 3- lb. Error Detection Techniques and Related Techniques (part 2) 3-3

Table 5-1. Types of Control Charts 5-19

Table B-1. List of Reviewed Documents B-1

Table B-2. Error Analysis Techniques Cited in Reviewed Documents B-5

FIGURES

Figure 5-1. Example np Control Chart - 5-20

Figure 5-2a. Example Bar Chart - Customer Ratings 5-22

Figure 5-2b. Example Bar Chart - Number of Modules with n Defects 5-23

Figure 5-3. Example Pareto Chart - Percentage of Defects by Type 5-24

Figure 5-4. Example Histogram 5-25

Figure 5-5. Scatter Diagram With Hypothetical Data - Complexity vs. Defect

Density 5-27

ix

1. OVERVIEW

This document provides guidance on software error analysis. Error analysis includes the

activities of detecting errors, of recording errors singly and across projects, and of analyzing

single errors and error data collectively. The purpose of error analysis is to provide assurance of the quality of high integrity software.

The software industry is currently still young, without sufficient knowledge and adequate

standards to guarantee fault-free software. Although research continues to identify better

processes for error prevention, with current practices, errors will likely be entered into the

software some time during development and maintenance. Hence, there is the need for error

analysis, to aid in detecting, analyzing, and removing the errors.

The main purpose of this study is to provide the software engineering community with current

information regarding error analysis, which will assist them to do the following:

• Understand how error analysis can aid in improving the software development process;

• Assess the quality of software, with the aid of error detection techniques;

• Analyze errors for their cause and then fix the errors; and

• Provide guidelines for the evaluation of high integrity software.

Section 2 discusses how error detection and analysis techniques can be used to improve the

quality of software. Section 3 provides a global description of the principal detection techniques

used in each software lifecycle phase and cost benefits for selected categories of these techniques.

Section 4 provides guidance on collecting individual error data and removing single errors. Section 5 describes techniques for the collection and analysis of sets of error data, including

statistical process control techniques and software reliability models. Section 6 provides a summary and recommendations based on this study of error analysis, and Section 7 provides a

list of references. Appendix A contains detailed descriptions of common error detection

techniques. Appendix B contains the results of a study of standards for high integrity software

to determine the extent of coverage of error analysis techniques.

The error detection techniques and statistical techniques described in this report are a representative sampling of the most widely-used techniques and those most frequently referenced

in standards, guidelines and technical literature. This report also describes the more common

software reliability estimation models, most which are described in the American Institute of

Aeronautics and Astronautics (AIAA) draft handbook for software reliability [AIAA]. Inclusion

of any technique in this report does not indicate endorsement by the National Institute of

Standards and Technology (NIST).

1-1
1.1. Definitions

Definitions of the following key terms used in this report are based on those in [lEEEGLOSS],

[JURAN], [FLORAC], [SQE], [SHOOMANl, and [NIST204]. However, this report does not

attempt to differentiate between "defect," "error," and "fault," since use of these terms within the

software community varies (even among standards addressing these terms). Rather, this report uses those terms in a way which is consistent with the definitions given below, and with other

references from which information was extracted.

anomaly. Any condition which departs from the expected. This expectation can come from

documentadon (e.g., requirements specificadons, design documents, user documents) or from

perceptions or experiences.

Note: An anomaly is not necessarily a problem in the software, but a deviadon from the

expected, so that errors, defects, faults, and failures are considered anomalies.

computed measure. A measure that is calculated from primitive measures.

defect. Any state of unfitness for use, or nonconformance to specification.

error. (1) The difference between a computed, observed, or measured value and the true,

specified, or theoredcally correct value or condition. (2) An incorrect step, process, or data

definidon. Often called a bug. (3) An incorrect result. (4) A human action that produces an

incorrect result. Note: One disdncdon assigns definition (1) to error, definidon (2) to fault, definition (3) to failure, and definition (4) to mistake.

error analysis. The use of techniques to detect errors, to esdmate/predict the number of errors, and to analyze error data both singly and collecdvely.

fault. An incorrect step, process, or data definidon in a computer program. See also: error.

failure. Discrepancy between the external results of a program's operation and the software

product requirements. A software failure is evidence of the existence of a fault in the software.

high integrity software. Software that must and can be trusted to work dependably in some

critical function, and whose failure to do so may have catastrophic results, such as serious injury,

loss of life or property, business failure or breach of security. Examples: nuclear safety systems,

medical devices, electronic banking, air traffic control, automated manufacturing, and military

systems.

primitive measure. A measure obtained by direct observation, often through a simple count

(e.g., number of errors in a module).

primitive metric. A metric whose value is directly measurable or countable.

1-2
measure. The numerical value obtained by either direct or indirect measurement; may also be

the input, output, or value of a metric.

metric. The definition, algorithm or mathematical function used to make a quantitative

assessment of product or process.

problem. Often used interchangeably with anomaly, although problem has a more negative

connotation, and implies that an error, fault, failure or defect does exist.

process. Any specific combination of machines, tools, methods, materials and/or people

employed to attain specific qualities in a product or service.

reliability (of software). The probabihty that a given software system operates for some time

period, without system failure due to a software fault, on the machine for which it was designed,

given that it is used within design limits.

statistical process control. The application of statistical techniques for measuring, analyzing,

and controlling the variation in processes.

1-3

2. INTRODUCTION TO SOFTWARE ERROR ANALYSIS

Software error analysis includes the techniques used to locate, analyze, and estimate errors and

data relating to errors. It includes the use of error detection techniques, analysis of single errors, data collection, metrics, statistical process control techniques, error prediction models, and

reliability models.

Error detection techniques are techniques of software development, software quality assurance (SQA), software verification, validation and tesdng used to locate anomalies in software products.

Once an anomaly is detected, analysis is performed to determine if the anomaly is an actual error, and if so, to identify precisely the nature and cause of the error so that it can be properly

resolved. Often, emphasis is placed only on resolving the single error. However, the single error could be representative of other similar errors which originated from the same incorrect

assumptions, or it could indicate the presence of serious problems in the development process.

Correcdng only the single error and not addressing underlying problems may cause further

complications later in the lifecycle.

Thorough error analysis includes the collection of error data, which enables the use of metrics

and stadstical process control (SPC) techniques. Metrics are used to assess a product or process

direcdy, while SPC techniques are used to locate major problems in the development process and

product by observing trends. Error data can be collected over the entire project and stored in an

organizational database, for use with the current project or future projects. As an example, SPC

techniques may reveal that a large number of errors are related to design, and after further

invesdgadon, it is discovered that many designers are making similar errors. It may then be

concluded that the design methodology is inappropriate for the particular application, or that

designers have not been adequately trained. Proper adjustments can then be made to the

development process, which are beneficial not only to the current project, but to future projects.

The collection of error data also supports the use of reliability models to esdmate the probability

that a system will operate without failures in a specified environment for a given amount of time.

A vendor^ may use software reliability esdmadon techniques to make changes in the testing

process, and a customer may use these techniques in deciding whether to accept a product.

The error data collected by a vendor may be useful to auditors. Auditors could request that

vendors submit error data, but with the understanding that confidendality will be maintained and

that recriminadons will not be made. Data collected from vendors could be used by the auditors

to establish a database, providing a baseline for comparison when performing evaluations of high

integrity software. Data from past projects would provide guidance to auditors on what to look

for, by identifying common types of errors, or other features related to errors. For example, it could be determined whether the error rates of the project under evaluation are within acceptable

bounds, compared with those of past projects.

In this report, the term "vendor" includes software developers.

2-1
2.1. Cost Benefits of Early Error Detection

Ideally, software development processes should be so advanced that no errors will enter a software system during development. Current practices can only help to reduce the number of

errors, not prevent all errors. However, even if the best practices were available, it would be

risky to assume that no errors enter a system, especially if it is a system requiring high integrity.

The use of error analysis allows for early error detection and correction. When an error made

early in the lifecycle goes undetected, problems and costs can accrue rapidly. An incorrectly

stated requirement may lead to incorrect assumptions in the design, which in turn cause subsequent errors in the code. It may be difficult to catch all errors during testing, since

exhaustive tesdng, which is testing of the software under all circumstances with all possible input

sets, is not possible [MYERS]. Therefore, even a cridcal error may remain undetected and be

delivered along with the final product. This undetected error may subsequently cause a system

failure, which results in costs not only to fix the error, but also for the system failure itself (e.g.,

plant shutdown, loss of life). •

Sometimes the cost of fixing an error may affect a decision not to fix an error. This is pardcularly true if the error is found late in the lifecycle. For example, when an error has caused

a failure during system test and the location of the error is found to be in the requirements or

design, correcting that error can be expensive. Sometimes the error is allowed to remain and the

fix deferred until the next version of the software. Persons responsible for these decisions may

justify them simply on the basis of cost or on an analysis which shows that the error, even when

exposed, will not cause a cridcal failure. Decision makers must have confidence in the analyses

used to idendfy the impact of the error, especially for software used in high integrity systems.

A strategy for avoiding the high costs of fixing errors late in the lifecycle is to prevent the

situation from occurring altogether, by detecdng and correcdng errors as early as possible.

Studies have shown that it is much more expensive to correct software requirements deficiencies

late in the development effort than it is to have correct requirements from the beginning [STSC].

In fact, the cost to correct a defect found late in the lifecycle may be more than one hundred

dmes the cost to detect and correct the problem when the defect was bom [DEMMY]. In addition to the lower cost of fixing individual errors, another cost benefit of performing error analysis early in development is that the error propagadon rate will be lower, resuldng in fewer

errors to correct in later phases. Thus, while error analysis at all phases is important, there is no

better time, in terms of cost benefit, to conduct error analysis than during the software

requirements phase.

2-2
2.2. Approach to Selecting Error Analysis Techniques

Planning for error analysis should be part of the process of planning the software system, along

with system hazard analysis^ and software criticality analysis. System hazard analysis is used

to identify potential events and circumstances that might lead to problems of varying degrees of

severity, from critical failures resulting in loss of life, to less serious malfuncdons in the system.

Software hazard analysis focuses on the role of the software relative to the hazards. Software

criticality analysis may use the results of system and software hazard analyses to identify the

software requirements (or design and code elements) whose erroneous implementation would

cause the most severe consequences. Criticality analysis may also be used to identify project

requirements that are essendal to achieving a working software system. Critical software

requirements are traced through the development process, so that developers can identify the

software elements which are most error-prone, and whose errors would be catastrophic.

The results of hazard analysis and cridcality analysis can be used to build an effective error analysis strategy. They aid in choosing the most appropriate techniques to detect errors during

the lifecycle (see sec. 3). They also aid in the planning of the error removal process (i.e., the

removal of individual errors, as described in sec. 4). Lastly, they aid in the selecnon of metrics,

statistical process control techniques, and software reliability estimation techniques, which are described in secdon 5. Error analysis efforts and resources can be concentrated in critical

program areas. Error analysis techniques should be chosen according to which type of errors they are best at locadng. The selecdon of techniques should take into account the error profile

and the characteristics of the development methodology. No project can afford to apply every

technique, and no technique guarantees that every error will be caught. Instead, the most

appropriate combination of techniques should be chosen to enable detection of as many errors

as possible in the earlier phases.

^In this report, system hazard analysis may also include analysis of threats to security features of the software.

2-3
I
3. TECHNIQUES FOR DETECTING ERRORS

Software development and maintenance involves many processes resulting in a variety of

products collectively essential to the operational software. These products include the statement

of the software requirements, software design descriptions, code (source, object), test documentation, user manuals, project plans, documentation of software quality assurance

activities, installation manuals, and maintenance manuals. These products will probably contain

at least some errors. The techniques described in this section can help to detect these errors. While not all products are necessarily delivered to the customer or provided to a regulatory

agency for review, the customer or regulatory agency should have assurance that the products

contain no errors, contain no more than an agreed upon level of estimated errors, or contain no

errors of a certain type.

This secdon of the report idendfies classes of error detection techniques, provides brief

descriptions of these techniques for each phase of the lifecycle, and discusses the benefits for

certain categories of these techniques. Detailed descripdons of selected techniques appear in

Appendix A. Detailed checklists provided in [NISTIR] idendfy typical problems that error detection techniques may uncover.

Error detection techniques may be performed by any organization responsible for developing and

assuring the quality of the product. In this report, the term "developer" is used to refer to developers, maintainers, software quality assurance personnel, independent software verification

and validadon personnel, or others who perform error detection techniques.

3.1. Classes of Error Detection Techniques

Error detecdon techniques generally fall into three main categories of analydc acdvities: stadc

analysis, dynamic analysis, and formal analysis. Stadc analysis is "the analysis of requirements,

design, code, or other items either manually or automatically, without execudng the subject of

the analysis to determine its lexical and syntacdc properdes as opposed to its behavioral

properties" [CLARK]. This type of technique is used to examine items at all phases of

development. Examples of stadc analysis techniques include inspecdons, reviews, code reading,

algorithm analysis, and tracing. Other examples include graphical techniques such as control

flow analysis, and finite state machines, which are often used with automated tools. Tradidonally, stadc analysis techniques are applied to the software requirements, design, and

code, but they may also be applied to test documentadon, particularly test cases, to verify

traceability to the software requirements and adequacy with respect to test requirements

[WALLACE].

Dynamic analysis techniques involve the execudon of a product and analysis of its response to

sets of input data to determine its validity and to detect errors. The behavioral properties of the

program are also observed. The most common type of dynamic analysis technique is testing.

Tesdng of software is usually conducted on individual components (e.g., subroutines, modules)

as they are developed, on software subsystems when they are integrated with one another or with

3-1
other system components, and on the complete system. Another type of testing is acceptance

testing, often conducted at the customer's site, but before the product is accepted by the

customer. Other examples of dynamic analyses include simulation, sizing and timing analysis,

and prototyping, which may be applied throughout the lifecycle.

Formal methods involve rigorous mathematical techniques to specify or analyze the software

requirements specification, design, or code. Formal methods can be used as an error detection

technique. One method is to write the software requirements in a formal specificadon language

(e.g., VDM, Z), and then verify the requirements using a formal verificadon (analysis) technique,

such as proof of correctness. Another method is to use a formal requirements specificadon

language and then execute the specification with an automated tool. This animation of the

specificadon provides the opportunity to examine the potendal behavior of a system without

completely developing a system first.

3.2. Techniques Used During the Lifecycle

Criteria for selection of techniques for this report include the amount of informadon available on

them, their citation in standards and guidelines, and their recent appearance in research ardcles

and technical conferences. Other techniques exist, but are not included in this report. Tables 3-

la and 3- lb provide a mapping of the error detecdon techniques described in Appendix A to software lifecycle phases. In these tables, the headings R, D, I, T, IC, and OM represent the

requirements, design, implementadon, test, installation and checkout, and operadon and

maintenance phases, respectively. The techniques and metrics described in this report are applicable to the products and processes of these phases, regardless of the lifecycle model

actually implemented (e.g., waterfall, spiral). Table B-2 in Appendix B lists which high integrity

standards cite these error detection techniques.

Table 3-la. Error Detection Techniques and Related Techniques (part 1)

TECHNIQUES R D I T I 0

C M

Algorithm analysis

Back-to-back testing

Boundary value analysis

Control flow analysis

Database analysis

Data flow analysis

Data flow diagrams

Decision tables (truth tables)

3-2
Table 3-lb. Error Detection Techniques and Related Techniques (part 2)

TECHNIQUES R D I T Ic

o

M

L/esK cnecKing ^coae reaaing^

niTor sccQing H

Finite state machines

Formal methods (formal verification)

Information flow analysis

Inspections H H

Interface analysis H

Interface testing

Mutation analysis B

Performance testing H

rToioiyping / animation m

Kegression diidiysis diiu lesiuig

Kecjuiremenis parsing

Reviews

oeiiaiuviiy aiiaiy!>i5

oiniUlallOIl

oii(ing oiiu uiiung analysis

OllClllg

Software sneak circuit analysis

Stress testing

Symbolic evaluation

Test certification

Tracing (traceability analysis)

Walkthroughs

3-3
3.2.1. Requirements

During the requirements phase, static analysis techniques can be used to check adherence to specification conventions, consistency, completeness, and language syntax. Commonly used

static analysis techniques during the requirements phase include control flow analysis, data flow

analysis, algorithm analysis, traceability analysis, and interface analysis. Control and data flow

analysis are most applicable for real time and data driven systems. These flow analyses employ

transformation of text describing logic and data requirements into graphic flows which are easier

to examine. Examples of control flow diagrams include state transition and transacdon diagrams.

Algorithm analysis involves rederivadon of equations or the evaluadon of the suitability of

specific numerical techniques. Traceability analysis involves tracing the requirements in the

software requirements specification to system requirements. The identified relationships are then

analyzed for correctness, consistency, completeness, and accuracy. Interface analysis in this

phase involves evaluadng the software requirements specificadon with the hardware, user, operator, and software interface requirements for correctness, consistency, completeness,

accuracy, and readability.

Dynamic analysis techniques can be used to examine information flows, funcdonal

interreladonships, and performance requirements. Simulation is used to evaluate the interacdons

of large, complex systems with many hardware, user, and other interfacing software components.

Prototyping helps customers and developers to examine the probable results of implemendng

software requirements. Examination of a prototype may help to idendfy incomplete or incorrect

requirements statements and may also reveal that the software requirements will not result in

system behavior the customer wants. Prototyping is usually worthwhile when the funcdons of

the computer system have not previously been used in automated form by the customer. In this case, the customer can change the requirements before cosdy implementation. Unless the project

is small or an automated method can be used to build a prototype quickly, usually only carefully

selected funcdons are studied by prototyping.

One approach for analyzing individual requirements is requirements parsing. This manual

technique involves examination to ensure that each requirement is defined unambiguously by a complete set of attributes (e.g., inidator of an acdon, source of the acdon, the acdon, the object

of the acdon, constraints). Because this technique identifies undefined attributes, it may prevent

release of incomplete requirements to the designers. In those cases where the requirements are

to be represented by a formal language specificadon, this analysis aids in clarifying a requirement

before its transformation.

Languages based on formal methods, i.e., mathematically based languages, may be used to specify system requirements. The act of specifying the software requirements in a formal

language forces reasoning about the requirements and becomes an error detection technique.

When requirements have been written in a formal language, the task of simulation may be easier.

Then, the behavior of the potential system can be observed through use of the simulation. It may

be the combination of formal specifications with other error detection techniques (e.g., control

flow analysis and data flow analysis) that provides the biggest payoff for using formal methods.

3-4
3.2.2. Design

Evaluation of the design provides assurance that the requirements are not misrepresented, omitted, or incompletely implemented, and that unwanted features are not designed into the product by

oversight. Design errors can be introduced by implementadon constraints relating to timing, data

structures, memory space, and accuracy.

Static analysis techniques help to detect inconsistencies, such as those between the inputs and

outputs specified for a high level module and the inputs and outputs of the submodules. The

most commonly used static analysis techniques during this phase include algorithm analysis,

database analysis, (design) interface analysis, and traceability analysis. As in the requirements

phase, algorithm analysis examines the correctness of the equations and numerical techniques,

but in addition, it examines truncation and rounding effects, numerical precision of word storage

and variables (single vs. extended-precision arithmedc), and data typing influences. Database

analysis is particularly useful for programs that store program logic in data parameters. Database

analysis supports verification of the computer security requirement of confidentiality, by checking

carefully the direct and indirect accesses to data. Interface analysis aids in evaluadng the

software design documentadon with hardware, operator, and software interface requirements for

correctness, consistency, completeness, and accuracy. Data items should be analyzed at each

interface. Traceability analysis involves tracing the software design documentation to the

software requirements documentadon and vice versa.

Commonly used dynamic analysis techniques for this phase include sizing and dming analysis,

prototyping, and simuladon. Sizing and dming analysis is useful in analyzing real-time programs

with response dme requirements and constrained memory and execution space requirements.

This type of analysis is especially useful for determining that allocations for hardware and

software are made appropriately for the design architecture; it would be quite cosdy to learn in system test that the performance problems are caused by the basic system design. An automated

simulation may be appropriate for larger designs. Prototyping can be used as an aid in examining

the design architecture in general or a specific set of functions. For large complicated systems

prototyping can prevent inappropriate designs from resulting in costly, wasted implementations.

Formal analysis involves tracing paths through the design specification and formulating a composite function for each, in order to compare these composite functions to that of the

previous level. This process ensures that the design continues to specify the same functional

solution as is hierarchically elaborated. This process can be applied manually, if the specification

is sufficiendy formal and exact, but is most feasible only for high level design specifications.

However, with automated tools, the functional effects of all levels of the design can be

determined, due to the speed and capacity of the tools for manipulating detailed specifications.

3.2.3. Implementation

Use of static analysis techniques helps to ensure that the implementation phase products (e.g.,

code and related documentation) are of the proper form. Static analysis involves checking that

3-5
the products adhere to coding and documentation standards or conventions, and that interfaces

and data types are correct. This analysis can be performed either manually or with automated

tools.

Frequendy used static analysis techniques during this phase include code reading, inspections,

walkthroughs, reviews, control flow analysis, database analysis, interface analysis, and traceability

analysis. Code reading involves the examinadon by an individual, usually an expert other than

the author, for obvious errors. Inspecdons, walkthroughs, and reviews, which are all used to detect logic and syntax errors, are effective forerunners to testing. As in previous phases, control

flow diagrams are used to show the hierarchy of main routines and their subfuncdons. Database

analysis is performed on programs with significant data storage to ensure that common data and

variable regions are used consistently between all calling routines; that data integrity is enforced

and no data or variable can be accidentally overwritten by overflowing data tables; and that data

typing and use are consistent throughout the program. With interface analysis, source code is evaluated with the hardware, operator, and software interface design documentadon, as in the

design phase. Traceability analysis involves tracing the source code to corresponding design

specificadons and vice versa.

One category of static analysis techniques performed on code is complexity analysis. Complexity

analysis measures the complexity of code based on specific measurements (e.g., number of

parameters passed, number of global parameters, number of operands/operators). Although not an error detecdon technique, complexity analysis can be used as an aid in identifying where use of error detecdon techniques should be concentrated and also in locating test paths and other

perdnent informadon to aid in test case generation.

Other static analysis techniques used during implementadon which aid in error detection include

software sneak circuit analysis and slicing. Software sneak circuit analysis is a rigorous,

language-independent technique for the detecdon of anomalous software (i.e., "sneaks") which

may cause system malfunction. The methodology involves creadon of a comprehensive

"pictorial" database using quasi-electrical symbology which is then analyzed using topological

and applicadon "clues" to detect faults in the code [PEYTON]. Slicing is a program

decomposition technique used to trace an output variable back through the code to idendfy all code statements relevant to a computauon in the program [LYLE]. This technique may be useful

to demonstrate functional diversity.

Dynamic analysis techniques help to determine the functional and computational correctness of

the code. Regression analysis is used to reevaluate requirements and design issues whenever any

significant code change is made. This analysis ensures awareness of the original system

requirements. Sizing and dming analysis is performed during incremental code development and

analysis results are compared against predicted values.

A formal method used in the implementation phase is proof of correctness, which is applied to code.

3-6
3.2.4. Test

Dynamic analysis in the test phase involves different types of testing and test strategies.

Traditionally there are four types of testing: unit, integration, system, and acceptance. Unit

testing may be either structural or functional testing performed on software units, modules, or

subroutines. Structural testing examines the logic of the units and may be used to support

requirements for test coverage, that is, how much of the program has been executed. Functional

testing evaluates how software requirements have been implemented. For functional testing, testers usually need no information about the design of the program because test cases are based on the software requirements.

Integration testing is conducted when software units are integrated with other software units or with system components. During integration testing, various strategies can be employed (e.g.,

top-down testing, bottom-up testing, sandwich testing) but may depend on the overall strategy

for constructing the system. Integration testing focuses on software, hardware, and operator

interfaces.

Both system testing and acceptance testing execute the complete system. The primary difference

is that the developer conducts system testing, usually in the development environment, while the

customer conducts acceptance testing (or commissions the developer to conduct the acceptance

testing in the presence of the customer). Acceptance testing is supposed to occur in a fully

operational customer environment, but in some cases (e.g., nuclear power plants, flight control

systems), some parts of the environment may need to be simulated.

For all four types of testing, different strategies may be used, according to the project's

characteristics. Some strategies include stress testing, boundary value testing, and mutation

testing. Operational profile testing allows testers to select input data that are of special interest to the customer. For example, input data that causes execution of the most frequently used

functions in operation may be the most important profile for testing for some systems. In other

cases, it may be more important to choose an input profile that should not occur in reality. For

nuclear power plants, this means choosing a profile that causes the software safety system to

react; the system responses can be examined to determine system behavior in adverse

circumstances.

A major problem with testing is knowing when to stop. Software reliability estimation

techniques, such as those described in section 5 of this report, can be used to estimate the number

of errors still present in the system, and to determine how much more testing is needed.

Sensitivity analysis, a promising technique emerging from the research community and entering

the marketplace, is intended to indicate where to test, and hence to determine how much to test [VOAS]. Because sensitivity analysis is derived from mutation testing which is intended for

detecting small changes to a program, this technique depends on code that is already "close to correct" for its effectiveness.

3-7
3.2.5. Installation and Checkout

During this phase, it is necessary to validate that the software operates correcdy with the

operational hardware system and with other software, as specified in the interface specifications.

It is also necessary to verify the correctness and adequacy of the installation procedures and

certify that the verified and validated software is the same as the executable code approved for

installation. There may be several installation sites, each with different parameters. It is necessary to check that the programs have been properly tailored for each site.

The most commonly used dynamic analysis techniques for this phase are regression analysis and

test, simulation, and test cerdfication. When any changes to the product are made during this

phase, regression analysis is performed to verify that the basic requirements and design

assumpdons affecdng other areas of the program have not been violated. Simulation is used to

test operator procedures and to isolate installation problems. Test cerdficadon, particularly in

critical software systems, is used to verify that the required tests have been executed and that the

delivered software product is identical to the product subjected to software verificadon and

validadon (V&V).

3.2.6. Operation and Maintenance

During operadon of an on-line condnuous system, test cases may be constructed that will check

periodically if the system is behaving as expected. For any software maintenance acdvity, error detection techniques should be selected as if the maintenance acdvity were a new development

activity, but considering the impact of new changes to the system. Use of traceability analysis on the software products, including test documentadon, is crucial to identifying the extent of use of any selected error detecdon technique on the total software system. Regression testing must

be applied in this phase.

3.3. Benefits of Classes of Error Detection Techniques

In the early days of computers, stadc analysis of software involved hours of tedious manual

checking of code for structural errors, syntax errors, and other types of errors. Today automation

handles the tedious bookkeeping in both design and code acdvides. In the past, manual reading

by an individual not only took longer, but may not always have been thorough. Design tools

force consistency to some extent and support other static analyses. Techniques such as control

flow analysis were difficult to perform manually, but with modern Computer Aided Software

Engineering (CASE) tools, most stadc analyses can be performed more quickly and efficiendy.

As the power and ease of use of the tools improve, then stadc analyses become more effecdve.

A tool commonly used to perform static analysis is the compiler, which can detect syntacdcal

code errors. The direct costs are the amount of electrical power and resources needed to conduct

the compiladon. However, not everyone agrees on the usefulness of compilers for producing

error-free code. Supporters of Cleanroom engineering, a methodology for developing and

assuring the quality of software, argue that the costs of rework and recompile are significant and

3-8
should not be ignored. They believe that complete reliance on the tools to perform some of the

intellectual work may reduce quality, because clean compilations can give a false sense of

complete correctness [MILLSl. With Cleanroom engineering, programmers do not compile their

code. Instead they spend more time on design, using a "box structure" method, and on analyzing

their own work. When the programmers are confident of their work, it is submitted to another

group who then compiles and tests the code. The Software Engineering Laboratory (SEL) at the

National Aeronaudcs and Space Administration Goddard Space Flight Center collected sufficient

data over 16 years to establish baselines for error and producdvity rates. In recent years, two

experiments were conducted on the Cleanroom approach [GREEN]. Results of the two

Cleanroom experiments compared with SEL baselines show a lower error rate in the finished

product and an increase in productivity across the Hfecycle.

Software inspection, another static technique, is dme consuming because it requires line by line, or graph by graph reading of the software element. Data collected from over 203 software

inspecdons at the Jet Propulsion Laboratory in Pasadena, California, showed a significantly

higher density of defects during requirements inspecdons [KELLY]. However, the defect

densides of the products decreased exponentially from the requirements phase to the coding

phase, implying that testing and rework will take less time. Code reading is another static analysis technique that has been shown in another SEL study to be quite effective [BASILI].

Researchers found that effectiveness increased with the experience level of the code readers, the reason being that experienced personnel were mentally executing the code. This technique may

be difficult to schedule and implement formally; usually it is used when a programmer seeks help

from a peer, and is conducted on small sections of code at a dme. Also, errors found by code

reading may not always be handled with a formal anomaly report.

Inspecdon and code reading have one drawback in common. The maximum benefits for these

techniques are achieved when they are performed on all elements of the design and code, which

tends to be dme-consuming. Because of the dme factor, they are usually conducted on only

small pordons of a program at a dme, usually in three- or four-hour meetings. When the

objecdve is to examine the endre program or many modules for global features, then other

techniques with specific objecdves (e.g., interface consistency, control flow, logic flow analysis)

are more appropriate. Many of the static analysis techniques are intended to be conducted by

individual members of a team, perhaps over days, and probably with automated support. There

may be interacdon among the team, especially to discuss possible anomalies. These types of

techniques are effective for examining the integration of design or code modules.

Dynamic analyses tend to use large amounts of computer resources and involve human effort to prepare, execute and analyze tests. Testing can never guarantee that a system is completely

correct, but it can demonstrate exactly what will occur under specific circumstances. Testing

helps to establish an operational profile under which the system will work correctly. Testing also

helps to uncover errors that were previously not discovered. Acceptance testing assures a customer that the software system will behave appropriately under specific circumstances as the

customer has requested. Some CASE tools provide test aids (e.g., test case generators, test result

capture, documentation). Although the power of modern computers has reduced execution time

3-9
of test cases, nevertheless, exhaustive testing with all possible inputs under all circumstances is

still not possible. In order to obtain maximum benefits from testing, careful planning and

development of test goals, and strategies to achieve those goals are required.

While some static and dynamic analyses have become easier to perform with CASE tools, CASE

technology has not eliminated all problems of software development and assurance. There are the problems of cost, methodology-dependence, and difficulty in understanding and using them.

Two other major problems with CASE include restrictions on using the environments when they

are not built on the concept of open systems [NIST187] and when information developed by the

tools cannot be readily exchanged among tools of different vendors.

While journal articles and other literature describing usage of formal methods are available, they

do not provide sufficient information to draw conclusions about the cost/quality benefits of using

formal methods for the assurance of the quality of software. A study of formal methods was

funded by NIST, the U.S. Naval Research Laboratory, and the Atomic Energy Control Board of

Canada, to determine whether the benefits of using formal methods are significant relative to the

costs of using them. The results of this study are published in [NISTGCR]. In the United

Kingdom, there is an existing standard for safety critical software used in defense equipment,

which requires the use of formal languages for specifications [MOD55].

The cost benefits of using specific error detection techniques or classes of techniques will differ

from project to project. A balanced error detection program will depend on many factors,

including the consequences of failure caused by an undetected error, the complexity of the

software system, the types of errors likely to be committed in developing specific software, the

effort needed to apply a technique, the automated support available, and the experience of the

development and assurance staff. Another factor to consider is the interplay among techniques

(e.g., whether the output of one technique can be used readily by another technique). If a specific error type is likely, then a technique known for finding that type of error should be

selected. The application of formal verification techniques is appropriate when failure of the

software would be disastrous. For planning a balanced program, an important requirement should

be to ensure that analyses are applied to all the software products at all phases of the lifecycle

in an orderly manner. The program should be evaluated frequently to ensure that the analyses

are being used correctly and are aiding in error detection. The SPC techniques described in

section 5 aid in this evaluation.

A final consideration for selecting techniques based on their cost benefit takes into account who

will be conducting the analysis, and under what circumstances. For auditors, techniques which

examine interfaces across the entire program, control flow, and critical operational paths are more

appropriate than those involving detailed line by line analysis (e.g., software code inspection).

When an anomaly is found, however, the auditors may choose to examine in greater detail the areas suspected of contributing to the anomaly.

3-10
4. REMOVAL OF ERRORS

This section describes the process of analyzing anomalies and removing errors. This is performed after an anomaly has been discovered using any error detection technique, such as those discussed in section 3. Analysis of an anomaly will not only aid in the removal of errors related to the anomaly, but will also help to detect other similar errors which have not yet

manifested themselves. In addition, information obtained from this analysis can provide valuable

feedback that may improve subsequent efforts and development processes in future projects.

The handling of an anomaly generally follows three steps: identificadon, investigation, and

resolution. However, exact procedures for dealing with an anomaly will depend on many factors.

First, it may be that the anomaly is not actually an error.'* For example, the anomaly may be

a result of misinterpreting test results. In these situations, an explanation about why the anomaly

is not an error should be recorded, and no further action is required. Second, the procedures will

depend on the activity used to detect the anomaly. For example, anomalies discovered during

walkthroughs and code reading are often fixed immediately, without having to go through the

formal error resolution process. During integration testing, all anomaly reports may be collected

and then addressed to locate probable cause and recommend fixes. Third, the severity level of

the anomaly will determine how soon the error should be fixed. Generally, the more severe the

error, the sooner it needs to be fixed.

The general policy for handling anomalies should include rules/regulations concerning the

administration of the entire error removal acdvity (e.g., who must fill out problem reports, where

or to whom this information is distributed, how to close out problem reports, who enters the

collected informadon into the error database). These issues are not addressed in this report,

because the policy will be specific to an organization.

General project information which supports the error removal process should be maintained. This

information may include, but is not limited to, descripdons of the design methodology, the

verification plan used in design, the test plan, the configuradon control plan, idenuficadon of

tools used to design and test software (e.g., CASE tools), and the programming language used.

4.1. Identification

As soon as an anomaly is detected, informadon about it should be recorded to help idendfy,

analyze, and correct the anomaly. Typically, this informadon is presented in an anomaly, or problem report. While the formats may differ, reports should include the following types of

information.

'^Or, the anomaly may be caused by a problem external to the software under analysis (e.g., the modem used

for testing was not configured properly), not by an error in the software. In this case, the information on the anomaly is sent to the responsible party, but is not further addressed by the error removal activity.

4-1
Locator. Identify the person(s) who discovered the anomaly including name, address, phone

number, email address, fax number, and company identification.

Date and Time. Specify the date and time that the anomaly occurred and/or was discovered.

Time can be specified by wall clock time, system time, or CPU dme. For distributed systems,

specify the time zone.

Activity. Identify the activity taking place at the time the anomaly was discovered. These

activities include error detection activities employed during the development and release of a product, including static and dynamic analysis, review, inspection, audit, simulation, timing

analysis, tesdng (unit, integradon, system, acceptance), compiling/assembling, and walkthrough.

Phase Encountered. Idendfy the lifecycle phase in which the anomaly was encountered (e.g.,

requirements, design, implementation, test, installation and checkout, and operation and

maintenance). If possible, specify the activity within the phase (e.g., during preHminary design

in the design phase).

Operadonal Environment. Specify the hardware, software, database, test support software,

platform, firmware, monitor/terminal, network, and peripherals being used.

Status of Product. Specify the effect of the problem on the product (e.g., unusable, degraded,

affected, unaffected).

Repeatability . Determine if the anomaly is a one-time occurrence, intermittent, recurring, or

reproducible.

Symptom . Describe the symptoms, which are indications that a problem exists (e.g.,

inconsistency between the design and the requirements, violadon of a constraint imposed by the

requirements, operadng system crash, program hang-up, input or output problem, incorrect

behavior or result, error message, inconsistent logic or behavior, infinite loop, and unexpected

abort).

Locadon of Symptom . The location of the anomaly can be in the actual product (hardware,

software, database, or documentadon), the test system, the platform, or in any development phase

product (e.g., specificadon, code, database, manuals and guides, plans and procedures, reports,

standards/policies). Idendfy the documentadon that was analyzed, or the code that was executed,

the tools and documentation used to support the activity. Identify the specific location(s) where

the anomaly is evident in documentation, or the test case in which the anomaly occurred.

Severity . Severity is a measure of the disruption an anomaly gives the user when encountered

during operation of the product. Severity can be divided into several levels, with the highest

level being catastrophic, and the lowest being at the annoyance level. A severity classificadon

system should be tailored to particular systems or class of systems. The number of levels and

4-2
the corresponding descriptions of the levels may vary,

is given below:

An example of a severity classification

Level 6

Level 5

Level 4

Level 3

Level 2

Level 1 Level 0

Critical. Major feature not working, system crashes, loss of data

Serious. Impairment of critical system functions, no workaround

Major. Workaround is difficult

Moderate. Workaround is simple

Cosmetic. Tolerable, or fix is deferrable

User misunderstanding

No problem (e.g., tesdng error)

4.2. Investigation

Following the identificadon stage, all errors should be investigated to obtain further information on the nature and cause in order to propose soludons for resolution action or corrective action.

Information that should be recorded during this stage include the following:

Phase Introduced. Identify the lifecycle phase in which the error was introduced. If possible,

specify the activity within the phase (e.g., during preliminary design in the design phase).

Tvpe. This attribute describes the type of error found, e.g., logic error, computational error, interface/dming error, data handling error, data error, documentation error, document quality error

(e.g., out of date, inconsistent, incomplete), and enhancement errors (e.g., errors caused by change

in requirements, errors caused by a previous fix).

Location of Error. The location of the error may be the same as the location of the symptom.

See Location of Symptom in section 4.1.

Cause. Typical causes of an error include human errors (e.g., misunderstanding, omission errors)

and inadequate methodology (e.g., incomplete inspection process, inappropriate design

methodology).

Units Affected. This attribute idendfies the software unit(s) affected by the error and its related

fix, e.g., which components, modules, or documents are affected.

Priori tv . Priority is the degree of importance that is given to the resolution of an error. Based

on the priority level, it is determined whether the error should be fixed immediately, eventually, or not at all (e.g., if error becomes obsolete as result of other changes). However, fixes should

be performed according to the software configuradon management policies of the project. The

relationship between the priority scale and the severity scale should be specified by the

administrative policy. An example of a priority scale is:

4-3
Level 5

Level 4

Level 3

Level 2

Level 1 Level 0

Resolve error immediately

Error gets high attention

Error will be placed in normal queue

Use v/orkaround or fix in the interim

Will be fixed last Will not be fixed

4.3. Resolution

Error resolution consists of the steps to correct the error. The policy of the project determines

if the person who investigates the error will also correct the error. The procedures for

distribution and retention of the error information is also identified by the policy. Typically, the

recipients of the error information are the project manager, SQA manager, corporate database

manager, and the customer. The amount of formalism (e.g., whether the plan needs to be

documented) depends on the scope, risk, and size of the project. For small errors in small

projects, this scheme may not be necessary.

4.3.1. Resolution Plan

The proposed procedures for resolution action should be documented in a resolution plan.

Item to Fix . Identify the item to be fixed (e.g., name, ID number, revision), the component

within the item, text description of the fix.

Estimated Date or Time. Specify the proposed dates for start and completion of the fix.

Personnel. Identify the manager, engineer, or other members responsible for performing the fix and for follow-up.

4.3.2. Resolution Action

The resolution action is the actual fix, i.e., making changes in the product to correct and remove

the error. The following information should be provided by the person(s) who perform the

resolution action, upon completion.

Date Completed. Specify the date when resolution action (fix) was completed.

Personnel. Identify the person(s) who fixed the error.

Time Length . Specify the number of minutes or hours required for the fix.

Size of Fix. Specify the size of the fix in terms of the number of source lines of code (SLOC)

added / modified, or the number of document pages added / modified.

4-4
4.3.3. Corrective Action

The corrective action stage is optional, because not all anomalies will require individual

corrective actions.

Standards, Policies or Procedures . Identify the standards, policies, or procedures to be revised,

created, or reinforced.

Other Action . This includes other revisions to the development process (e.g., implementing

training, reallocation of people or resources, and improving or enforcing audit activities).

4.3.4. Follow-up

For all errors, there should be a follow-up to verify that the necessary resolution action or

corrective action has been performed correctly.

Personnel. Identify the person or organization that performed follow-up of resolution action

and/or corrective action.

Verification of Action. Confirm that the "right fix" has been performed, that the error has

actually been removed, and that the fix has not adversely affected other areas of the software.

Disposition. Describe the status of the anomaly, whether it is closed (resolution was

implemented and verified, or not within scope of project), deferred, merged with another

problem, or referred to another project.

Closeout. Identify procedures for retention of the error data.

4.4. Use of Individual Error Data

The data that is collected for the purpose of removing a single error can be used for other

purposes. This data can aid in removing all errors similar to the original error. In addition to making improvements in the product, data on single errors can be used to improve the current

development process. For instance, if many errors are found to be requirements errors, this may

prompt a change to the requirements specification process. Data on single errors can also be

used in measurement and statistical process control activities such as those discussed in section

5. For example, the data can be used to calculate measures or it can be used as input to control

charts. Finally, individual error data can be entered into an error database, in order to maintain an error history of all projects in the organization.

4-5

5. TECHNIQUES FOR THE COLLECTION AND ANALYSIS OF ERROR DATA

Techniques for collecting and analyzing sets of error data during the lifecycle aid in understanding, evaluating and improving the development and maintenance process or aid in evaluating or estimating product quality. Software measures provide insights about both process

and product. Measures may feed into statistical process control (SPC) techniques; SPC

techniques may be used for both process and product evaluation. Software reliability estimation

techniques are usually applied only to the product. Most of these techniques operate on error history profiles of error data discovered by error detection techniques.

This section addresses only the error aspects of these techniques. Other information may need

to be collected when making major decisions to change a policy or development activity (e.g.,

project information, customer requirements, company policy, methodologies being used, tools

being used, number of people using a certain technique). These types of information are not

discussed in this report.

5.1. Error History Profile / Database

An error history profile is needed to perform error analysis effectively. An organizational

database can be used both to track the status of a project and to track the use of error analysis

techniques. Data collected for the purpose of resolving single errors (e.g., source, cause, type,

severity), should be placed in the error database to enable the establishment of anomaly histories.

Other data collected specifically for the purpose of measurement or statistical process control

should also be entered into the database. The database serves as a baseline for validation as well

as for improvement. Past mistakes can be avoided from lessons learned. Maintaining a database

serves the following purposes:

• To identify which development processes work well (or poorly) for an application domain,

• To support process improvement for the current project as well as for future projects,

To identify whether the processes are being applied properly (or at all),

• To identify error classes by cause.

To estimate the error growth rate throughout development, and therefore to be able to adjust plans for assurance activities, and

To measure the quality of the software at dehvery.

Error data collected from an error detection technique in one phase can support process

improvement in an earlier lifecycle phase (for future projects), as well as in a later phase. For

5-1
example, in a presentation at a COMPASS Conference,^ one panel member explained that

analysis of the data collected from code inspections at his organization revealed that a high

percentage of the code errors were the result of errors in the software requirements specification. In response to this finding, the organization began investigating the use of formal languages for

specifying software requirements. This example demonstrates the necessity of collecting and

analyzing data for both error removal and process improvement.

Data histories can help managers to recognize when there is a significant deviation from project

plans during development. Past error data can be used to estimate the number of expected errors

at different times in the development cycle. For instance, if the reported error count for a particular product was smaller than was expected, compared with similar past projects, this may

suggest that the development team generated an unusually low number of errors. However,

further investigation may reveal that the project was behind schedule, and to save time, planned

inspections were not held. Thus, many existing errors remained undetected, so that the low error count did not reflect the true quality of the product. This example illustrates how a history

profile enables an organization to recognize and correct a process problem to avoid delivering a product with residual errors.

5.2. Data Collection Process

This section describes the process of collecting error data for a specific purpose (e.g., to use with

control charts). Some of the error data may include data previously collected during error

detection. This data can be retrieved from the organizational database, or can be collected

directly upon discovery or during resolution of an error. Data must be collected properly in order

for any error analysis technique to be effective. The recommended steps of the data collection

process are listed below [AIAA]:

1. Establish the objectives.

2. Prepare a data collection plan. The plan may include the following recommended

elements:

Data definition and type. Specify/define the data to be collected and the type (i.e., attribute or variable data). An attribute is a characteristic that an item may or may not

possess. It is obtained by noting the presence or absence of a characteristic and counting occurrences of the characteristic with a specified unit. For example: a module may or may not contain a defect. This type of data takes on only discrete (integer) values.

Variable data is obtained by recording a numerical value for each item observed.

Variable data can be either continuous or discrete. Examples: cost of fixing an error (continuous), lines of code (discrete).

^Dr. John Kelly, of the Jet Propulsion Laboratory, was a member of the "Formal Methods in Industry" panel

at the 1992 COMPASS Conference, held at NIST in Gaithersburg, Maryland on June 15-18, 1992. The panel

discussion was not documented in the conference proceedings.

5-2
Analysis technique. Identify the technique requiring the data. Each technique has unique

data requirements, so the technique to be used should be specified prior to data collection.

Measurement method. Measurements can be taken by equipment, ob.servation, or

selecting data from existing records. The reliability, determined by accuracy and

precision, of the measurement method must be established prior to data collection.

Sampling Procedure. The data collection interval, amount of data to be collected, and the

sampling method should be specified (e.g., random sampling using a random number

table). When determining the data collection interval, issues such as process volume,

process stability, and cost should be considered.

Personnel. Identify persons responsible for specific data collection tasks.

Forms for data reporting (e.g., electronic spreadsheet, paper forms, etc.).

Recording and processing of data. One method for processing data is blocking, the

separating of data into potential comparison categories during the recording of data.

Blocking can be accomplished by recording each category separately, or through labeling

information that enables future sorting.

Monitoring. Describe how the data collection process is to be monitored.

3. Apply tools. Automated tools should be considered whenever possible, in order to minimize impact on the project's schedule. Factors to consider include the following:

availability of the tool, reliability of the tool, cost of purchasing or developing the tool,

and whether it can handle any necessary adjustments.

4. Provide training. Once tools and plans are in place, training should be provided to ensure

that data collectors understand the purpose of the measurements and know explicitly what

data is to be collected.

5. Perform trial run. A trial run of the data collection plan should be made to resolve any

problems or misconceptions about the plan. This can save vast amounts of time and

effort.

6. Implement the plan. Collect the data and review them promptly, so that problems can be

resolved before the disappearance of information required to resolve them (e.g., if test results on a screen are not saved).

7. Monitor data collection. Monitor the process to ensure that objectives are met and that

procedures are implemented according to the data collection plan.

8. Use the data. Use the data as soon as possible to achieve maximum benefit.

5-3
9. Provide feedback to all involved. Those involved need to know what impact their efforts

had, and the end result. This will enable them to understand the purpose of their efforts

and agree to undertake similar tasks in the future.

Additional recommendations for the data collection process are listed below [ROOK]:

• Data collection should be integrated into the development process (e.g., as part of the

quality management system).

• Data collection should be automated whenever possible.

• Time-scales between data collection and data analysis should be minimized.

• Data should be treated as a company resource and facilities should be available to keep

historical records of projects as well as to monitor current projects.

• The problem of motivating personnel to keep accurate records should not be

underestimated. Proper training and quick analysis facilities are essential, but are not

sufficient. ,

5.3. Metrics

Within the software engineering community, there is much confusion and inconsistency over the use of the terms metric and measure. In this report, a metric is defined to be the mathematical

definition, algorithm, or function used to obtain a quantitative assessment of a product or process.

The actual numerical value produced by a metric is a measure. Thus, for example, cyclomatic

complexity is a metric, but the value of this metric is the cyclomatic complexity measure.

Data on individual errors (see sec. 4) can be used to calculate metrics values. Two general

classes of metrics include the following:

management metrics, which assist in the control or management of the development

process; and

quality metrics, which are predictors or indicators of the product qualities

Management metrics can be used for controlling any industrial production or manufacturing

activity. They are used to assess resources, cost, and task completion. Examples of resourcerelated metrics include elapsed calendar time, effort, and machine usage. Typical metrics for

software estimate task completion include percentage of modules coded, or percentage of

statements tested. Other management metrics used in project control include defect-related

metrics. Information on the nature and origin of defects are used to estimate costs associated

with defect discovery and removal. Defect rates for the current project can be compared to that

of past projects to ensure that the current project is behaving as expected.

5-4
Quality metrics are used to estimate characteristics or qualities of a software product. Examples

of these metrics include complexity metrics, and readability indexes for software documents. The

use of these metrics for quality assessment is based on the assumptions that the metric measures some inherent property of the software, and that the inherent property itself influences the

behavioral characteristics of the final product.

Some metrics may be both management metrics and quality metrics, i.e., they can be used for

both project control and quality assessment. These metrics include simple size metrics (e.g., lines

of code, number of funcdon points) and primidve problem, fault, or error metrics. For example,

size is used to predict project effort and time scales, but it can also be used as a quality predictor,

since larger projects may be more complex and difficult to understand, and thus more error- prone.

A disadvantage of some metrics is that they do not have an interpretadon scale which allows for

consistent interpretation, as with measuring temperature (in degrees Celsius) or length (in meters).

This is particularly true of metrics for software quality characteristics (e.g., maintainability,

reliability, usability). Measures must be interpreted relarively, through comparison with plans

and expectations, comparison with similar past projects, or comparison with similar components

within the current project. While some metrics are mathemadcally-based, most, including

reliability models, have not been proven.

Since there is virtually an infinite number of possible metrics, users must have some criteria for

choosing which metrics to apply to their particular projects. Ideally, a metric should possess all of the following characteristics:

• Simple - definidon and use of the metric is simple

• Objecdve - different people will give identical values; allows for consistency, and

prevents individual bias

• Easily collected - the cost and effort to obtain the measure is reasonable

• Robust - metric is insensitive to irrelevant changes; allows for useful comparison

• Valid - metric measures what it is supposed to; this promotes trustworthiness of the measure

Within the software engineering community, two philosophies on measurement are embodied by

two major standards organizations. A draft standard on software quality metrics sponsored by

the Institute for Electrical and Electronics Engineers Software Engineering Standards

Subcommittee supports the single value concept. This concept is that a single numerical value can be computed to indicate the quality of the software; the number is computed by measuring

and combining the measures for attributes related to several quality characteristics. The

international community, represented by the ISO/IEC organization through its Joint Technical

Committee, Subcommittee 7 for software engineering appears to be adopting the view that a range of values, rather than a single number, for representing overall quality is more appropriate.

5-5
5.3.1. Metrics Throughout the Lifecycle

Metrics enable the estimation of work required in each phase, in terms of the budget and

schedule. They also allow for the percentage of work completed to be assessed at any point

during the phase, and establish criteria for determining the completion of the phase.

The general approach to using metrics, which is applicable to each lifecycle phase, is as follows:

[ROOK]

• Select the appropriate metrics to be used to assess activities and outputs in each phase of

the lifecycle.

• Determine the goals or expected values of the metrics.

Determine or compute the measures, or actual values.

• Compare the actual values with the expected values or goals.

• Devise a plan to correct any observed deviadons from the expected values.

Some complications may be involved when applying this approach to software. First, there will

often be many possible causes for deviadons from expectadons and for each cause there may be

several different types of correcdve acdons. Therefore, it must be determined which of the

possible causes is the actual cause before the appropriate correcdve action can be taken. In addition, the expected values themselves may be inappropriate, when there are no very accurate

models available to esdmate them.

In addition to monitoring using expected values derived from other projects, metrics can also

identify anomalous components that are unusual with respect to other components values in the

same project. In this case, project monitoring is based on internally generated project norms,

rather than estimates from other projects.

The metrics described in the following subsecdons are defined in [ROOK], [IEEE982.2] and

[AIRFORCE], [SQE], and [ZAGE] and comprise a representative sample of management and

quality metrics that can be used in the lifecycle phases to support error analysis. This section

does not evaluate or compare metrics, but provides definidons to help readers decide which

metrics may be useful for a pardcular application.

5.3.1.1. Metrics Used in All Phases

Primidve metrics such as those listed below can be collected throughout the lifecycle. These

metrics can be plotted using bar graphs, histograms, and Pareto charts as part of statisdcal

process control. The plots can be analyzed by management to idendfy the phases that are most

5-6
error prone, to suggest steps to prevent the recurrence of similar errors, to suggest procedures for

earlier detection of faults, and to make general improvements to the development process.

Problem Metrics

Primitive problem metrics .

Number of problem reports per phase, priority, category, or cause Number of reported problems per time period

Number of open real problems per time period

Number of closed real problems per time period

Number of unevaluated problem reports

Age of open real problem reports

Age of unevaluated problem reports

Age of real closed problem reports

Time when errors are discovered

Rate of error discovery

Cost and Effort Metrics

Primitive cost and effort metrics. Time spent

Elapsed time

Staff hours

Staff months

Staff years

Change Metrics

Primitive change metrics .

Number of revisions, additions, deletions, or modifications

Number of requests to change the requirements specification and/or design during

lifecycle phases after the requirements phase

Fault Metrics

Primitive fault metrics . Assesses the efficiency and effectiveness of fault

resolution/removal activities, and check that sufficient effort is available for fault

resolution/removal.

Number of unresolved faults at planned end of phase

Number of faults that, although fully diagnosed, have not been corrected, and

number of outstanding change requests

5-7
Number of requirements and design faults detected during reviews and

walkthroughs

5.3.1.2. Requirements Metrics

The main reasons to measure requirements specifications is to provide early warnings of quality

problems, to enable more accurate project predictions, and to help improve the specifications.

Primitive size metrics . These metrics involve a simple count. Large components are assumed to have a larger number of residual errors, and are more difficult to understand

than small components; as a result, their reliability and extendibility may be affected.

Number of pages or words

Number of requirements

Number of functions

Requirements traceability . This metric is used to assess the degree of traceability by

measuring the percentage of requirements that has been implemented in the design. It is also used to identify requirements that are either missing from, or in addition to the

original requirements. The measure is computed using the equation: RT = R1/R2 x 100%, where Rl is the number of requirements met by the architecture (design), and R2

is the number of original requirements. [IEEE982.2]

Completeness (CM). Used to determine the completeness of the software specification

during requirements phase. This metric uses eighteen primitives (e.g., number of

functions not satisfactorily defined, number of functions, number of defined functions,

number of defined functions not used, number of referenced functions, and number of

decision points). It then uses ten derivatives (e.g., functions satisfactorily defined, data

references having an origin, defined functions used, reference functions defined), which

are derived from the primitives. The metric is the weighted sum of the ten derivatives

expressed as CM = E WjDj, where the summation is from i=l to i=10, each weight Wj has

a value between 0 and 1, the sum of the weights is 1, and each Dj is a derivative with a value between 1 and 0. The values of the primitives also can be used to identify problem

areas within the requirements specification. [IEEE982.2]

Fault-days number. Specifies the number of days that faults spend in the software

product from its creation to their removal. This measure uses two primitives: the phase,

date, or time that the fault was introduced, and the phase, date, or time that the fault was

removed. The fault days for the ith fault, (FDj), is the number of days from the creation

of the fault to its removal. The measure is calculated as follows: FD = X FDj.

This measure is an indicator of the quality of the software design and development

process. A high value may be indicative of untimely removal of faults and/or existence

of many faults, due to an ineffective development process. [IEEE982.2]

5-8
Function points . This measure was originated by ASlan Albreciit at IBM in the late 1970's, and was further developed by Charles Symons. It uses a weighted sum of the

number of inputs, outputs, master files and inquiries in a product to predict development

size [ALBRECHT]. To count function points, the first step is to classify each component

by using standard guides to rate each component as having low, average, or high

complexity. The second basic step is to tabulate function component counts. This is done by entering the appropriate counts in the Function Counting Form, multiplying by

the weights on the form, and summing up the totals for each component type to obtain

the Unadjusted Function Point Count. The third step is to rate each application

characteristic from 0 to 5 using a rating guide, and then adding all the ratings together

to obtain the Characteristic Influence Rating. Finally, the number of function points is calculated using the equation

Function pts. = Unadjusted function * (.65 + .01 * Character Influence Rating)

point count [SQE]

5.3.1.3. Design Metrics

The main reasons for computing metrics during the design phase are the following: gives early

indication of project status; enables selection of alternative designs; identifies potential problems

early in the lifecycle; limits complexity; and helps in deciding how to modularize so the resulting

modules are both testable and maintainable. In general, good design practices involve high

cohesion of modules, low coupling of modules, and effective modularity. [ZAGE]

Size Metrics

Primitive size metrics. These metrics are used to estimate the size of the design or design

documents.

Number of pages or words

DLOC (lines of PDL)

Number of modules

Number of functions

Number of inputs and outputs

Number of interfaces

(Estimated) number of modules (NM). Provides measure of product size, against which

the completeness of subsequent module based activities can be assessed. The estimate

for the number of modules is given by, NM = S/M, where S is the estimated size in LOC,

M is the median module size found in similar projects. The estimate NM can be

compared to the median number of modules for other projects. [ROOK]

5-9
Fault Metrics

Primitive fault metrics. These metrics identify potentially fault-prone modules as early

as possible. [ROOK]

Number of faults associated with each module

Number of requirements faults and structural design faults detected during detailed

design

Complexity Metrics

Primitive complexity metrics . Identifies early in development modules which are potentially complex or hard to test. [ROOK]

Number of parameters per module

Number of states or data partitions per parameter

Number of branches in each module

Coupling. Coupling is the manner and degree of interdependence between software

modules [IEEE982.2]. Module couphng is rated based on the type of couphng, using a standard rating chart, which can be found in [SQE]. According to the chart, data

coupling is the best type of coupling, and content coupling is the worst. The better the

coupling, the lower the rating. [SQE], [ZAGE]

Cohesion . Cohesion is the degree to which the tasks performed within a single software

module are related to the module's purpose. The module cohesion value for a module

is assigned using a standard rating chart, which can be found in [SQE]. According to the

chart, the best cohesion level is functional, and the worst is coincidental, with the better

levels having lower values. Case studies have shown that fault rate correlates highly with

cohesion strength. [SQE], [ZAGE]

(Structural) fan-in / fan-out. Fan-in/fan-out represents the number of modules that

call/are called by a given module. Identifies whether the system decomposition is adequate (e.g., no modules which cause botdenecks, no missing levels in the hierarchical

decomposition, no unused modules ("dead" code), identification of critical modules). May

be useful to compute maximum, average, and total fan-in/fan-out. [ROOK], [IEEE982.2]

Information flow metric (C) . Represents the total number of combinadons of an input source to an output desdnation, given by, C = Q x (fan-in x fan-out)^ where Q is a code

metric, which may be omitted. The product inside the parentheses represents the total number of paths through a module. [ZAGE]

5-10
Design Inspection Metrics

Staff hours per major defect detected . Used to evaluate the efficiency of the design

inspection processes. The following primitives are used: time expended in preparation

for inspection meeting (Tl), time expended in conduct of inspection meeting (T2),

number of major defects detected during the ith inspection (Sj), and total number of

inspecdons to date (I). The staff hours per major defect detected is given below, with the

summations being from i=l to i=I. M = I(T1 + T2),

This measure is applied to new code, and should fall between three and five. If there is significant deviation from this range, then the matter should be investigated. (May be

adapted for code inspecdons). [IEEE982.2]

Defect Density (DP). Used after design inspections of new development or large block

modifications in order to assess the inspecdon process. The following primidves are used: total number of unique defects detected during the ith inspecdon or ith lifecycle

phase (Dj), total number of inspecdons to date (I), and number of source lines of design

statements in thousands (KSLOD). The measure is calculated by the ratio

DD = X Dj where the sum is from i=l to i=I. KSLOD

This measure can also be used in the implementadon phase, in which case the number of

source lines of executable code in thousands (KSLOC) should be subsntuted for KSLOD.

[IEEE982.2]

Test Related Metrics.

Test related primidves. Checks that each module will be / has been adequately tested, or assesses the effecdveness of early tesdng acdvides. [ROOK]

Number of integradon test cases planned/executed involving each module

Number of black box test cases planned/executed per module

Number of requirements faults detected during testing (also re-assesses quality of

requirements specificadon)

5.3.1.4. Implementation Metrics

Metrics used during the implementation phase can be grouped into four basic types: size metrics,

control structure metrics, data structure metrics, and other code metrics.

5-11
Size Metrics '

Lines of Code (LOC) . Although lines of code is one of the most popular metrics, it has no standard definition.^ The predominant definition for line of code is "any line of a program text that is not a comment or blank line, regardless of the number of statements or fragments of statements on the line." [SQE] It is an indication of size, which allows

for estimation of effort, timescale, and total number of faults. For the same application,

the length of a program partly depends on the language the code is written in, thus

making comparison using LOC difficult. However, LOC can be a useful measure if the

projects being compared are consistent in their development methods (e.g., use the same

language, coding style). Because of its disadvantages, the use of LOC as a management

metric (e.g., for project sizing beginning from the requirements phase) is controversial,

but there are uses for this metric in error analysis, such as to estimate the values of other

metrics. The advantages of this metric are that it is conceptually simple, easily

automated, and inexpensive. [SQE]

Halstead software science metrics . This set of metrics was developed by Maurice

Halstead, who claimed they could be used to evaluate the mental effort and time required

to create a program, and how compactly a program is expressed. These metrics are based on four primitives listed below:

ni = number of unique operators

n2 = number of unique operands

Nj = total occurrences of operators

N2 = total occurrences of operands

The program length measure, N, is the sum of N, and Nj. Other software science metrics

are listed below. [SQE]

Vocabulary: n = n, + n2

Predicted length: = (n, * log2n,) + (n2 * logjUj)

Program volume: V = N * log2n

Effort: E = (n,N2Nlog2n)/(2n2)

Time: T = E/B ; Halstead B=18

Predicted number of bugs: B = V/3000

Control Structure Metrics

Number of entries/exits per module. Used to assess the complexity of a software

architecture, by counting the number of entry and exit points for each module. The

equation to determine the measure for the ith module is simply mj = e, + x,, where ej is

^The SEI has made an effort 10 provide a complete definition for LOC. Sec [PARK].

5-12
the number of entry points for the ith module, and is the number of exit points for the

ith module. [ffiEE982.2]

Cyclomatic complexity (C) . Used to determine the structural complexity of a coded

module in order to limit its complexity, thus promoting understandability. In general,

high complexity leads to a high number of defects and maintenance costs. Also used to identify minimum number of test paths to assure test coverage. The primitives for this measure include the number of nodes (N), and the number of edges (E), which can be

determined from the graph representing the module. The measure can then be computed

with the formula, C = E - N + 1. [IEEE982.2], [SQE]

Data Structure Metrics

Amount of data. This measure can be determined by primitive metrics such as Halstead's

Uj and N2, number of inputs/outputs, or the number of variables. These primitive metrics can be obtained from a compiler cross reference. [SQE]

Live variables . For each line in a section of code, determine the number of live variables

(i.e., variables whose values could change during execution of that section of code). The

average number of live variables per line of code is the sum of the number of live

variables for each line, divided by the number of lines of code. [SQE]

Variable scope. The variable scope is the number of source statements between the first and last reference of the variable. For example, if variable A is first referenced on line

10, and last referenced on line 20, then the variable scope for A is 9. To determine the

average variable scope for variables in a particular section of code, first determine the

variable scope for each variable, sum up these values, and divide by the number of

variables [SQE]. With large scopes, the understandability and readability of the code is reduced.

Variable spans. The variable span is the number of source statements between successive

references of the variable. For each variable, the average span can be computed. For

example, if the variable X is referenced on lines 13, 18, 20, 21, and 23, the average span

would be the sum of all the spans divided by the number of spans, i.e., (4+ 1 +0+ 1 )/4 =

1.5. With large spans, it is more likely that a far back reference will be forgotten. [SQE]

5.3.1.5. Test Metrics

Test metrics may be of two types: metrics related to test results or the quality of the product

being tested; and metrics used to assess the effectiveness of the testing process.

5-13
PRODUCT METRICS

Defect/Error/Fault Metrics

Primitive defect/error/fault metrics . These metrics can be effectively used with SPC

techniques, such as bar charts, and Pareto diagrams. These metrics can also be used to form percentages (e.g., percentage of logic errors = number of logic errors h- total number

of errors).

Number of faults detected in each module

Number of requirements, design, and coding faults found during unit and

; integration testing

Number of errors by type (e.g., number of logic errors, number of computational

errors, number of interface errors, number of documentation errors)

Number of errors by cause or origin

Number of errors by severity (e.g., number of cridcal errors, number of major

errors, number of cosmedc errors)

Fault density (FD) . This measure is computed by dividing the number of faults by the

size (usually in KLOC, thousands of lines of code). It may be weighted by severity using

the equation

FD^ = (Wi S/N + W2 A/N + W3 M/N) / Size

where N = total number of faults

S = number of severe faults

A = number of average severity faults

M = number of minor faults

Wj = weighting factors (defaults are 10, 3, and 1)

FD can be used to perform the following: predict remaining faults by comparison with

expected fault density; determine if sufficient tesdng has been completed based on

predetermined goals; establish standard fault densides for comparison and predicdon.

[IEEE982.2], [SQE]

Defect age. Defect age is the dme between when a defect is introduced to when it is detected or fixed. Assign the numbers 1 through 6 to each of the lifecycle phases from

requirements to operation and maintenance. The defect age is then the difference of the

numbers corresponding to the phase introduced and phase detected. The average defect

age = L (phase detected - phase introduced), the sum being over all the defects. [SQE]

number of defects

Defect response time . This measure is the time between when a defect is detected to when it is fixed or closed. [SQE]

5-14
Defect cost . The cost of a defect may be a sum of the cost to analyze the defect, the cost

to fix it, and the cost of failures already incurred due to the defect. [SQEJ

Defect removal efficiency (PRE) . The DRE is the percentage of defects that have been

removed during a process, computed with the equation:

DRE = Number of defects removed x 100%

Number of defects at start of process

The DRE can also be computed for each lifecycle phase and plotted on a bar graph to show the reladve defect removal efficiencies for each phase. Or, the DRE may be

computed for a specific process (e.g., design inspecdon, code walkthrough, unit test, sixmonth operation, etc.). [SQE]

PROCESS METRICS

Test case metrics

Primirive test case metrics.

Total number of planned white/black box test cases run to completion

Number of planned integration tests run to compledon

Number of unplanned test cases required during test phase

Coverage metrics^

Statement coverage. Measures the percentage of statements executed (to assure that each

statement has been tested at least once). [SQE]

Branch coverage. Measures the percentage of branches executed. [SQE]

Path coverage. Measures the percentage of program paths executed. It is generally

impracdcal and inefficient to test all the paths in a program. The count of the number

of paths may be reduced by treadng all possible loop iteradons as one path. [SQE] Path

coverage may be used to ensure 100 percent coverage of critical (safety or security

related) paths.

Data flow coverage. Measures the definidon and use of variables and data structures. [SQE]

^Commercial tools are available for statement coverage, branch coverage, and path coverage, but only private

tools exist for data flow coverage. [BEIZER] Coverage tools report the p)ercentage of items covered and lists what

is not covered. [SQE]

5-15
Test coverage. Measures the completeness of the testing process. Test coverage is the

percentage of requirements implemented (in the form of defined test cases or functional

capabilities) multiplied by the percentage of the software structure (in units, segments,

statements, branches, or path test results) tested. [AIRFORCE]

Failure metrics

Mean time to failure (MTTF). Gives an estimate of the mean time to the next failure, by

accurately recording failure times t^, the elapsed time between the ith and the (i-l)st

failures, and computing the average of all the failure times. This metric is the basic

parameter required by most software reliability models. High values imply good

reliability. [IEEE982.21

Failure rate . Used to indicate the growth in the software reliability as a function of test time and is usually used with reliability models. This metric requires two primitives: tj, the observed time between failures for a given severity level i, and f|, the number of

failures of a given severity level in the ith time interval. The failure rate ^(t) can be

estimated from the reUability function R(t), which is obtained from the cumulative

probability distribution F(t) of the time until the next failure, using a software reliabihty

estimation model, such as the non-homogeneous Poisson process (NHPP) or Bayesian

type model. The failure rate is X{t) = -1/R(t) [dR(t)1 where R(t) = 1 - F(t). tIEEE982.2]

' dt

Cumulative failure profile . Uses a graphical technique to predict reliability, to estimate

additional testing time needed to reach an acceptable reliability level, and to identify

modules and subsystems that require additional testing. This metric requires one

primitive, fj, the total number of failures of a given severity level i in a given time

interval. Cumulative failures are plotted on a time scale. The shape of the curve is used

to project when testing will be complete, and to assess reliability. It can provide an

indication of clustering of faults in modules, suggesting further testing for these modules.

A non-asymptotic curve also indicates the need for continued testing. [IEEE982.2]

5.3.1.6. Installation and Checkout Metrics

Most of the metrics used during the test phase are also applicable during installation and

checkout. The specific metrics used will depend on the type of testing performed. If acceptance

testing is conducted, a requirements trace may be performed to determine what percentage of the

requirements are satisfied in the product (i.e., number of requirements fulfilled divided by the

total number of requirements). *

5.3.1.7. Operation and Maintenance Metrics

Every metric that can be applied during software development may also be applied during

maintenance. The purposes may differ somewhat. For example, requirements traceability may

5-16
be used to ensure that maintenance requirements are related to predecessor requirements, and that

the test process covers the same test areas as for the development. Metrics that were used during

development may be used again during maintenance for comparison purposes (e.g., measuring

the complexity of a module before and after modification). Elements of support, such as customer perceptions, training, hotlines, documentation, and user manuals, can also be measured.

Change Metrics

Primitive change metrics .

Number of changes

Cost/effort of changes

Time required for each change

LOC added, deleted, or modified

Number of fixes, or enhancements

Customer Related Metrics

Customer ratings. These metrics are based on results of customer surveys, which ask

customers to provide a rating or a satisfaction score (e.g., on a scale of one to ten) of a vendor's product or customer services (e.g., hodines, fixes, user manual). Radngs and

scores can be tabulated and plotted in bar graphs.

Customer service metrics.

Number of hotline calls received

Number of fixes for each type of product

Number of hours required for fixes

Number of hours for training (for each type of product)

5.4. Statistical Process Control Techniques

Statistical process control is the applicadon of stadstical methods to provide the information

necessary to continuously control or improve processes throughout the entire lifecycle of a product [OPMC]. SPC techniques help to locate trends, cycles, and irregularides within the

development process and provide clues about how well the process meets specifications or requirements. They are tools for measuring and understanding process variation and

distinguishing between random inherent variations and significant deviations so that correct

decisions can be made about whether to make changes to the process or product.

To fully understand a process, it is necessary to determine how the process changes over time.

To do this, one can plot error data (e.g., total number of errors, counts of specific types of errors) over a period of time (e.g., days, weeks, Ufecycle phases) and then interpret the resulting pattern.

If, for instance, a large number of errors are found in a particular phase, an investigation of the

5-17
activities in that phase or preceding ones may reveal that necessary development activities were

omitted (e.g., code reviews were not conducted during the implementation phase). A plot of the sources of errors may show that a pardcular group is the most frequent source of errors. Further

investigadon may confirm that members of the group do not have sufficient experience and

training. A plot of the number of specific types of errors may show that many errors are related

to incorrect or unclear requirements specifications (e.g., requirements are written in a way that

consistendy causes misinterpretadons, or they fail to list enough conditions and restricdons).

This would indicate that the process of requirements specificadon needs to be modified.

There are several advantages to using SPC techniques. First, errors may be detected earlier or prevented altogether. By monitoring the development process, the cause of the error (e.g.,

inadequate standards, insufficient training, incompatible hardware) may be detected before

addidonal errors are created. Second, using SPC techniques is cost-effecdve, because less effort

may be required to ensure that processes are operadng correcdy than is required to perform

detailed checks on all the outputs of that process. Thus, higher quality may be achieved at a lower development expense. Finally, use of SPC techniques provides quandtadve measures of

progress and of problems so less guesswork is required [DEMMYJ.

Despite the advantages, there are also several potential disadvantages. To be successful, SPC

requires discipline, planning, condnuous commitment to the dmely soludon of process problems,

and frequent access to informadon from the software lifecycle process [DEMMY].

5.4.1. Control Chart

The primary stadsrical technique used to assess process variation is the control chart. The control

chart displays sequendal process measurements reladve to the overall process average and control

limits. The upper and lower control limits establish the boundaries of normal variadon for the

process being measured. Variadon within control limits is attributable to random or chance

causes, while variadon beyond control limits indicates a process change due to causes other than

chance — a condition that may require investigation. [OPMC] The upper control limit (UCL) and

lower control limit (LCL) give the boundaries within which observed fluctuadons are typical and

acceptable. They are usually set, respectively, at three standard deviations above and below the

mean of all observadons. There are many different types of control charts, pn, p, c, etc., which

are described in Table 5-1. This section is based on [OPMC], [SMITH], [CAPRIO], and

[JURAN].

5-18
Table 5-1. Types of Control Charts

TYPF X-^XjLjV OFSCRTPTION XvlX X XWl 1 IMP! FMFNTATION

np number of nonconforming unils

(e.g., number of defective

units)

The number of units in each sample with the selected characteristic is plotted; sample size is constant.

P

fraction of nonconforming units

(e.g., fraction of defective

units)

For each sample, the fraction nonconforming,

obtained by dividing the number nonconforming

by the total number of units observed, is plotted; sample size can change.

c number of nonconformities

(e.g., number of errors)

For each sample, the number of occurrences of

the characteristic in a group is plotted; sample

size is constant.

u number of nonconformities per

unit

(e.g., number of errors per unit)

For each sample, the number of nonconformities

per unit, obtained by dividing the number of

nonconformities by the number of units observed is nlotted* samnle size can change

X single observed value The value for each sample of size 1 is plotted.

XB X-Bar For each sample, the mean of 2 to 10

observations (4 or 5 are optimal) is plotted.

R range The difference between the largest and smallest values in each sample is plotted.

XM median The median of each sample is plotted.

MR moving range The difference between adjacent measurements

in each sample is plotted.

Implementation

1. Identify the puipose and the characteristics of the process to be monitored.

2. Select the appropriate type of control chart based on the type of characteristic measured,

the data available, and the purpose of the application.

3. Determine the sampling method (e.g., number of samples (n), size of samples, time

frame).

4. Collect the data.

5. Calculate the sample statistics: average, standard deviation, upper and lower control

limits.

6. Construct the control chart based on sample statistics.

7. Monitor the process by observing pattern of the data points and whether they fall within

control limits.

5-19
Interpretation

The existence of outliers, or data points beyond control limits, indicates that non-typical

circumstances exist. A run, or consecutive points on one side of the average line (8 in a row,

or 11 of 12, etc.) indicates a shift in process average. A sawtooth pattern, which is a successive

up and down trend with no data points near the average line, indicates overadjustment or the

existence of two processes. A trend, or steady inclining or declining progression of data points

represents gradual change in the process. A hug, in which all data points fall near the average

line, may indicate unreliable data. A cycle, or a series of data points which is repeated to form

a pattern, indicates a cycling process.

UCL = 10.13

U

03 Q

pn - 4.15

LCL = -1.83

o o-o O O I ©-Q

to b-o p t)

&-0 b

to 6-0 p &

to-©' 0

Work Week

Figure 5-1. Example np Control Chart - Number of Defects Per Work Week.

Application Examples

Control charts are applicable to almost any measurable activity. Some examples for software

include the following: number of defects/errors, training efforts, execution time, and number of

problem reports per time period. An example of a np control with hypothetical data is shown

in Figure 5-1. In this example, the number of samples (n) is 100. Each data point represents

the number of defects found in the software product in a work week.

5-20
5.4.2. Run Chart

A run chart is a simplified control chart, in which the upper and lower control limits are omitted.

The purpose of the run chart is more to determine trends in a process, rather than its variation.

Although very simple, run charts can be used effectively to monitor a process, e.g., to detect

sudden changes and to assess the effects of corrective actions. Run charts provide the input for

establishing control charts after a process has matured or stabilized in time. Limitations of this

technique are that it analyzes only one characteristic over time, and it does not indicate if a single

data point is an outlier. This section is based on [OPMC] and [CAPRIOJ.

Implementation

1. Decide which outputs of a process to measure.

2. Collect the data.

3. Compute and draw the average line. 4. Plot the individual measurements chronologically.

5. Connect data points for ease of interpretation.

Interpretation

See Interpretation for Control Charts.

Application Examples

Run charts are applicable to almost any measurable activity. Some examples for software include

the following: number of defects/errors, number of failures, execution time, and downtime.

5.4.3. Bar Graph

A bar graph is a frequency distribution diagram in which each bar represents a characteristic/

attribute, and the height of the bar represents the frequency of that characteristic. The horizontal

axis may represent a continuous numerical scale (e.g., hours), or a discrete non-numerical scale

(e.g., Module A, Module B or Requirements Phase, Design Phase). Generally, numerical-scale

bar charts in which the bars have equal widths are more useful for comparison purposes;

numerical- scale bar charts with unequal intervals can be misleading because the characteristics

with the largest bars (in terms of area) do not necessarily have the highest frequency. This

section is based on [SMITH].

Implementation

1. Define the subject and purpose.

2. Collect the data. Check that the sample size is sufficient.

3. Sort the data by frequency (or other measure) of characteristics.

5-21
4. For numerical-scale bar charts, determine the number of bars and the width of the bars

(class width), by trying series of class widths, avoiding too fine or too coarse a

granularity. The class widths in a chart may be all the same, or they may vary (as in fig.

5-2b), depending on how one wants to show the distribution of the data.

5. Construct the chart and draw the bars. The height of a bar represents the frequency of the

corresponding characteristic.

Interpretation

In a simple bar graph in which the characteristics being measured are discrete and non-numerical

(e.g., in fig. 5-2a) or if each bar has the same width, the measures for each characteristic can be

compared simply by comparing the heights of the bars. For numerical-scale graphs with unequal

widths, one can still compare the heights of the bars, but should remember not to interpret large

bars as necessarily meaning that a large proportion of the entire population falls in that range.

Average Rating

5.0

I

1 3.0 '

I 1 I 1 2.0

1.0

I 1 ABC D E F G

Evaluation Category

Figure 5-2a. Example Bar Chart - Customer Ratings (5 is best, 1 is worst).

Application Examples

Bar graphs are useful for analyzing and displaying many different types of data. It is mosdy

used to compare the frequencies of different attributes. For example, in Figure 5-2a, it is used

to plot the average customer rating for each evaluation category (e.g., customer service, hotlines,

overall satisfaction). The graph shows clearly that Category D has the highest rating. Figure 5-

2b illustrates how numerical-scale bar charts can be used for software analysis. Based on

hypothetical data, it shows the percentage of modules falling in each defect range. For instance,

the graph shows that 30% of all modules contain 10-20 defects and 5% contain 20-25 defects.

Other examples of characteristics that may be plotted include: number or percentage of errors by lifecycle phase, by type, or by cause, and number or percentage of problem reports by phase

or by type. See also section 5.3.1.1 on primitive problem metrics for additional examples.

5-22
Modules (%)

60

40

20

30

20 20

10 10

5

0 10 20 30 40 50

Number of Defects (n)

Figure 5-2b. Example Bar Chart - Number of Modules with n Defects

5.4.4. Pareto Diagram

A Pareto diagram is a special use of the bar graph in which the bars are arranged in descending

order of magnitude. The purpose of Pareto analysis, using Pareto diagrams, is to identify the

major problems in a product or process, or more generally, to identify the most significant causes

for a given effect. This allows a developer to prioritize problems and decide which problem area

to work on first. This section is based on [OPMC] and [CAPRIO],

Implementation

1. Follow the steps for constructing a bar graph, except that the bars should be in descending

order of magnitude (height).

2. Determine the "vital few" causes by drawing a cumulative percent line and applying the

20/80 rule.

3. Compare and identify the major causes. Repeat process until root cause of the problem

is revealed.

Interpretation

Pareto analysis is based on the 20/80 rule, which states that approximately 20% of the causes (the

"vital few") account for 80% of the effects (problems). The "vital few" can be determined by

drawing a cumulative percent line and noting which bars are to the left of the point marking 80%

of the total count. The vital few are usually indicated by significantly higher bars and/or a

relatively steep slope of the cumulative percent line. In Figure 5-2, the vital few are logic,

computational, and interface errors since 80% of the errors are found in these modules. By

5-23
knowing the primary causes of a problem or effect, the developer can decide where efforts should

be concentrated.

Application Examples

Most data that can be plotted on a non-numerical scale bar graph can also be plotted on a Pareto

diagram. Examples include: number or percentage of errors by type, by cause, or by lifecycle

phase, and number or percentage of problem reports by type or by lifecycle phase.

Type of Error

Figure 5-3. Example Pareto Chart - Percentage of Defects by Type.

5.4.4. Histogram

A histogram is a frequency distribution diagram in which the frequencies of occurrences of the

different variables being plotted are represented by bars. The purpose is to determine the shape

of the graph relative to the normal distribution (or other distributions). It is often confused with

a bar graph, in which the frequency of a variable is indicated by the height of the bars. In a histogram, the frequency is indicated by the area of the bar. Histograms can only be used with

variable data, which require measurements on a continuous scale. Only one characteristic can be shown per histogram, and at least 30 observations representing homogenous conditions are needed. This section is based on [OPMC], [CAPRIO], and [FREEDMAN].

r

5-24
Implementation

Define the subject and purpose.

Collect the data and organize from smallest to largest values. Check that sample size is sufficient.

Calculate the range (r), i.e. the difference between the largest and smallest values.

Decide arbitrarily on the number of bars (k), usually betv/een 7 and 13. To make bars of equal width, use the equation, w = r/k to calculate the interval or width

(w) of the bars.

Sort the data into the appropriate intervals and count the number of data points that fall in each interval.

Calculate the frequencies (actual counts or percent) for each interval.

Draw the bars. The height of the bar is calculated by dividing the frequency by w, the

width of the bar (in horizontal units).

10

8

6

4

2

20 30

20

10

10

0 10 20 30 40

Number of Defects (n)

Figure 5-4. Example Histogram - Number of Modules with n Defects.

50

Interpretation

A histogram is a fi"equency distribution, in which the area of each bar is always proportional to thd actual percentage of the total falling in a given range. For example, Figure 5-4 shows that

30% of all modules contain 10-20 defects, indicated by the largest bar. Both Figure 5-4 and

Figure 5-2a are plotted with the same data. Note the difference in the relative size of the bars.

If the bars are of equal width, then the histogram is equivalent to a bar graph, in which the

relative size of the bars depends only on their heights. A histogram can be compared to the

normal distribution (or other distribution). For example, if the graph is off-center or skewed, this

may indicate that a process requires adjustment.

5-25
Application Examples

Histograms are essentially used for the same applications as bar graphs, except that the horizontal

scale in a histogram must be numerical, usually representing a continuous random variable. See

Application Examples for Bar Graphs.

5.4.5. Scatter Diagram

A scatter diagram is a plot of the values of one variable against those of another variable to determine the relationship between them. This technique was popularized by Walter Shewhart

at Bell Laboratories. Scatter diagrams are used during analysis to understand the cause and effect

relationship between two variables. They are also called correlation diagrams. This section is based on [KITCHENHAM] , [OPMC], and [CAPRIO].

Implementation

1. Define the subject and select the variables.

2. Collect the data.

3. Plot the data points using an appropriate scale.

4. Examine the pattern to determine whether any correlation exists (e.g., posidve, negadve).

For a more precise specificadon of the reladonship, regression, curve fitting or smoothing

techniques can be applied.

Interpretarion

If the data points fall approximately in a straight line, this indicates that there is a linear

relationship, which is posidve or negadve, depending on whether the slope of the line is posidve

or negadve. Further analysis using the method of least squares can be performed. If the data

points form a curve, then there is a non-linear relationship. If there is no apparent pattern, this

may indicate no reladonship. However, another sample should be taken before making such a conclusion.

Applicadon Examples

The following are examples of pairs of variables that might be plotted:

• Complexity vs. defect density (example shown in fig. 5-5)

• Effort vs. duradon (of an acdvity)

• Failures vs. dme

• Failures vs. size

• Cost vs. dme

5-26
X Median

19

Y Median

22

• • •

40

CYCLOMATIC COMPLEXITY

Figure 5-5. Scatter Diagram With Hypothetical Data - Complexity vs. Defect Density.

5.4.6. Method of Least Squares (Regression Technique)

This technique can be used in conjunction with scatter diagrams to obtain a more precise

relationship between variables. It is used to determine the equation of the regression line, i.e., the line that "best fits" the data points. With this equation, one can approximate values of one

variable when given values of the other. The equation of the line is Y = a + bX, where a and

b are constants which minimize S, the sum of squares of the deviations of all data points from

the regression line. For any sample value Xj of X, the expected Y value is a + bX|. This section

is based on [OPMC], [CAPRIO], and [SMITH].

Implementation

1. Collect n data values for each of the 2 variables, X and Y, denoted by Xj, Xj,..., x^ and

2. Minimize S = L (y; - a - bXj)^ by first taking the partial derivative of S with respect to a and then with respect to b, setting these derivatives to zero, and then solving for a and

b.

5-27
3. The results obtained from steps should be the following,^ where Xg = Zx/n and =

Xy/n:

b = X (Xi - XhKY, - Yb) a = Y3-bX3

5: (Xi - x^r

Interpretation

The constant a represents the intercept of the regression line, i.e., the value of Y when X is 0,

and

b represents the slope of the regression line. The idea of this technique is to minimize S, so that

all data points will be as close to the regression line as possible. The reason for taking the

squares of the deviations, rather than simply the deviations, is so that positive and negative

deviations will not cancel each other when they are summed. It would also be possible to sum

the absolute values of the deviations, but absolute values are generally harder to work with than

squares.

Application Examples

See Application Examples for Scatter Diagrams.

5.5. Software Reliability Estimation Models

"Reliability" is used in a general sense to express a degree of confidence that a part or system

will successfully function in a certain environment during a specified time period [JURAN].

Software reliability estimation models can predict the future behavior of a software product,

based on its past behavior, usually in terms of failure rates. Since 1972, more than 40 software

reliability estimadon models have been developed, with each based on a certain set of

assumptions characterizing the environment generating the data. However, in spite of much

research effort, there is no universally applicable software reliability estimadon model which can

be trusted to give accurate predicdons of reliability in all circumstances [BROCKLEHURST].

It is usually possible to obtain accurate reliability predictions for software, and to have confidence

in their accuracy, if appropriate data is used [ROOK]. Also, the use of reliability esdmation

models is sdll under acdve research, so improvements to model capability are likely. Recent

work by Littlewood (1989), for example, involves the use of techniques for improving the

accuracy of predicdons by learning from the analysis of past errors [ROOK], and recalibradon

[BROCKLEHURST].

'Another form of the equation for b, which is often easier to compute is b = ZXiY, - nX.YB

IX? - n(X,y

5-28
Some problems have been encountered by those who have tried to apply reliability estimation

models in practice. The algorithms used to estimate the model parameters may fail to converge.

When they do, the estimates can vary widely as more data is entered [DACSj. There is also the

difficulty of choosing which reliability model to use, especially since one can not know a priori

which of the many models is most suitable in a particular context [BROCKLEHURSTj. In general, the use of these models is only suitable for situations in which fairly modest reliability

levels are required [ROOK].

There are three general classes of software reliability estimation models: nonhomogeneous

Poisson process (NPIPP) models, exponential renewal NHPP models, and Bayesian models.

Some of the more common reliability estimation models are described below [DUNN], [LYUj.

• Jelinski-Moranda (JM). One of the earliest models, it assumes the debugging process is purely deterministic, that is, that each defect in the program is equally likely to produce

failure (but at random times), and that each fix is perfect, i.e., introduces no new defects.

It also assumes that the failure rate is proportional to the number of remaining defects and

remains constant between failures. This model tends to be too optimistic and to underestimate the number of remaining faults; this effect has been observed in several

actual data sets.

• Goel-Okumoto (GO). This model is similar to JM, except it assumes the failure rate (number of failure occurrences per unit of time) improves continuously in time.

• Yamada Delayed S-Shape. This model is similar to GO, except it accounts for the

learning period that testers go through as they become familiar with the software at the

start of testing.

• Musa-Okumoto (MO). This NHPP model is similar to GO, except it assumes that later

fixes have a smaller effect on a program's reliability than earlier ones. Failures are assumed to be independent of each other.

Geometric. This model is a variation of JM, which does not assume a fixed, finite

number of program errors, nor does it assume that errors are equally likely to occur.

• Schneidewind. Similar to JM, this model assumes that as testing proceeds with time, the error detection process changes, and that recent error counts are usually more useful than

earlier counts in predicting future counts.

Bayesian Jelinski-Moranda (BJM) Model. This model is similar to JM, except that it uses a Bayesian inference scheme, rather than maximum likelihood. Although BJM does not

drastically underestimate the number of remaining errors, it does not offer significant

improvement over JM. Actual reliability predictions of the two models are usually very

close.

5-29
Littlewood. This model attempts to answer the criticisms of JM and BJM by assuming

that different faults have different sizes, i.e., they contribute unequally to the unreliability

of the software. This assumption represents the uncertainty about the effect of a fix.

Littlewood-Verrall (LV). This model takes into account the uncertainty of fault size and

efficacy of a fix (i.e., a fix is of uncertain magnitude and may make a program less

reliable), by letdng the size of the improvement in the failure rate at a fix vary randomly.

Brooks and Modey (BM). The BM binomial and Poisson models attempt to consider that

not all of a program is tested equally during a tesdng period and that only some portions

of the program may be available for tesdng during its development.

Duane. This model assumes that the failure rate changes condnuously in time, i.e., it follows a nonhomogeneous Poisson process. The cumuladve failure rate when plotted

against the total tesdng dme on a In-ln graph follows a straight line. The two parameters

for the equation of the line can be derived using the method of least squares.

Implementation

The following is a generic procedure for estimating software reliability [AIAA]. It can be

tailored to a specific project or lifecycle phase; thus some steps may not be used in some

applications.

1. Identify the application. The description of the application should include, at a minimum,

the identification of the application, the characteristics of the application domain that may

affect reliability, and details of the intended operation of the application system.

2. Specify the requirement. The reliability requirement should be specific enough to serve

as a goal (e.g., failure rate of 10'^ per hour).

3. Allocate the requirement. The reliability requirement may be distributed over several

components, which should be identified.

4. Define failure. A specific failure definition is usually agreed upon by testers, developers,

and users prior to the beginning of testing. The definition should be consistent over the

life of the project. Classification of failures (e.g., by severity) is continuously negotiated.

5. Characterize the operational environment. The operational environment should be

described in terms of the system configuration (arrangement of the system's components),

system evolution and system operational profile (how system will be used).

6. Select tests. The test team selects the most appropriate tests for exposing faults. Two

approaches to testing can be taken: testing duplicates actual operational environments as

5-30
closely as possible; or testing is conducted under more severe conditions than expected

in normal operational environments, so that failures can occur in less time.

7. Select the models. The user should compare the models prior to selection based on the

following criteria: predictive validity, ease of parameter measurement, quality of the

model's assumptions, capability, applicability, simplicity, insensitivity to noise, and

sensitivity to parameter variations.

8. ' Collect data. See section 5.2.

9. Determine the parameters. There are three common methods of estimating the parameters

from the data: method of moments, least squares, and maximum likelihood. Each of

these methods has useful attributes, but maximum likelihood esdmation is the most

commonly used approach. However, for some models, the maximum likelihood method

does not yield equadons for the parameters in closed form, so instead numerical methods

(e.g., Newton's method) must be used [ROME]. As stated previously, some datasets may

cause the numerical methods not to converge. There exist automated software reliability

engineering tools, which are capable of performing parameter esdmation.

10. VaUdate the model. The model should be continuously checked to verify that it fits the

data, by using a predicdve validity criteria or a tradidonal statisncal goodness-of-fit test

(e.g., Chi-square).

11. Perform analysis. The results of software reliability estimadon may be used for several

purposes, including, but not limited to, esdmadng current reliability, forecasting

achievement of a reliabihty goal, establishing conformance with acceptance criteria,

managing entry of new software features or new technology into an existing system, or supporting safety certificadon.

Interpretation

A disadvantage of these models is that they rely on testing and hence are used rather late in the

development life cycle. The models are usually time based, that is, the probability is based on

time to failure. Research is needed to identify how to use more valuable parameters with these

models. See [ROOK]

Application Examples

Applicability of the models should be examined through various sizes, structures, functions and

application domains. An advantage of a reliability model is its usability in different development

and operational environments, and in different lifecycle phases. Software reliability models

should be used when dealing with the following situations:

5-31
Evolving software (i.e., software that is incrementally integrated during tesdng)

Classificadon of failure severity

Incomplete failure data

Hardware execudon rate differences

Muldple installations of the same software

Project environments depardng from model assumpdons

5-32
6. SUMMARY

Error analysis for software consists of many activities to assure the quality of delivered software.

The activities include error detection, analysis, resolution on an individual level and also on a collective level. In the latter case, the collective data may be used to locate common errors within a product, to identify areas for improvement in software development and maintenance,

and to identify areas for improvement in error analysis.

Many activities of error analysis may be conducted during the early phases of the software

development lifecycle to prevent error propagation and to reduce costs of fixing the errors at a

later dme in the lifecycle. Finding the root cause of an error discovered in system test may

require analysis of code, design specifications, software requirements documentation, and perhaps

analysis and test documentadon. Correcdon of the error results in additional verification and

testing activities through the lifecycle products. The time spent initially in preparing correctly

stated software requirements will pay off in reduced dme needed for rework in the later phases.

The error detection techniques described in this report are a representadve sampling of the most

widely-used error detection techniques and those most frequently referenced in standards,

guidelines and technical literature. The report also describes some techniques which represent

new approaches and are not yet widespread. The techniques include those that examine software

products without execudng the software, those that execute (or simulate the execudon oO the

software, and those that are based on mathemadcal analysis and proof of correctness techniques.

Evidence of the effecdveness of any of these techniques may be hard to find. Journal articles

report success of some techniques, but most often anecdotal evidence is provided through

conference presentadons and discussions among colleagues.

With many techniques to choose from, appropriate selection for a specific project depends on the

characteristics of the project, such as the types of problems most likely to occur. Other selection

criteria, which are outside the scope of this report, include parameters like development

processes, skill of the technical staff, project resources, and quality requirements of the project.

The study of standards for high integrity software reported in [NUREG, NIST204] indicated that

these standards are beginning to require techniques of all kinds with some guidelines attempting

to base the requirement on the quality requirements and problem types of the software project.

An examination of approximately 50 standards, draft standards, and guidelines indicates that these

documents vary widely in their recommendations for error analysis.

Error detection is only one activity of error analysis. Information about the detected error must

be reported and delivered to any persons responsible for correcting the error, managing the

project, analyzing the data for process improvement and identifying similar problems in the

product. Individual problem reports may be collected and analyzed using statistical process

control techniques, to determine and monitor the efficiency and adequacy of the development

process. Findings which result from using SPC techniques should be used as feedback to improve the development process for the current, as well as future, products. Data on single

6-1
errors is also used in estimating software reliability and in predicting the number of errors (at

later stages in the lifecycle).

Vendors should use error analysis to collect error data and to build corporate memory databases

for use across projects. They may use the error information to identify appropriate techniques

for similar projects and to better understand how to produce quality software systems.

Regulatory agencies should also consider establishing and maintaining a database of error analysis data for software systems. Willingness of vendors to participate must include

mechanisms to assure confidentiality of proprietary information and that vendor data will not be

used in a recriminatory sense. The database must contain both developmental and operational

error data for effective use. Vendors, auditors, regulators, and the software engineering

community may all benefit from use of error databases.

Auditors may use the information in the database to identify the most error-prone features of

specific high integrity systems and may ensure that their audits examine these features carefully.

The auditors may use the data to identify acceptance limits on different aspects of the high

integrity software safety system.

Regulators may use the information from a database in several ways. First, over time, it may

become apparent that some error analysis techniques are more effective than others with respect

to a given type of problem. It may also become apparent that problems in these areas occur most

often with certain development practices or occur less frequently with other development

practices. This knowledge may influence recommendations in regulatory documents.

Finally, careful analysis of the information in the database may enable the software engineering

community in general to identify research needs for software development practices to prevent

specific problems from occurring and error analysis techniques to locate problems as soon as possible in the software lifecycle.

6-2
7. REFERENCES

[AIAA]

R-013-1992, "Recommended Practice for Software Reliability," American Institute of

Aeronautics and Astronautics, Space-Based Observation Systems Committee on Standards,

Software Reliability Working Group, 1992, c/o Jim French, AIAA Headquarters, 370

L'Enfant Promenade, SW, Washington, DC 20024-25 18W.

[AIRFORCE]

AFSCP 800-14, Air Force Systems Command, Software Quality Indicators, "Management

Quality Insight," Department of the Air Force, January 20, 1987.

[ALBRECHT]

Albrecht, Allan J. and John E. Gaffney, Jr., "Software Function, Source Lines of Code,

and Development Effort Predicdon: A Software Science Validation," IEEE Transactions on Software Engineering, Vol. SE-9, No. 6, November, 1983.

[ANS104]

ANSI/ANS-10.4-1987, "Guidelines for the Verification and Validation of Sciendfic and

Engineering Computer Programs for the Nuclear Industry," American Nuclear Society,

May 13, 1987.

[ASMENQA2]

ASME NQA-2a-1990 Part 2.7, "Quality Assurance Requirements for Nuclear Facility

Applications," The American Society of Mechanical Engineers, November 1990.

[BASILI]

Basili, V.R. and R.W. Selby, "Comparing the Effecdveness of Software Testing

Strategies," IEEE Transacdons on Software Engineering, IEEE Computer Society,

Volume SE-13, Number 12, December 1987, pp. 1278-1296.

[BEIZER]

Beizer, Boris, Software Tesdng Techniques, Van Nostrand Reinhold Company, New

York, 1983.

[BROCKLEHURST]

Brocklehurst, S., P. Y. Chan, Bev Littlewood, and John Snell, "Recalibrating Software

Reliability Models," IEEE Transacdons on Software Engineering, Vol. 16, No. 4, 1990.

[CAPRIO]

Caprio, William H., "The Tools for Quality," Total Quality Management Conference, Ft. Belvoir, Virginia, July 13-15, 1992.

7-1
[CLARK]

Clark, Peter, and Bard S. Crawford, Evaluation and Validation (E&V) Reference Manual, TASC No. TR-5234-3, Version 3.0, February 14, 1991.

[DACS]

"Software Reliability Models," DACS Newsletter, Data & Analysis Center for Software,

Volume X, Number 2, Summer, 1992.

[DEMILLO]

DeMillo, Richard A. et al. Software Testing and Evaluation , The Benjamin/Cummings

Publishing Company, Inc., Menlo Park, CA, 1987.

[DEMMY]

Demmy, W. Steven and Arthur B. Petrini, "Statistical Process Control in Software Quality

Assurance," Proceedings of the 1989 National Aerospace and Electronics Conference, NAECON, May 22-26, 1989, Dayton, OH, IEEE, Inc., Piscataway, NJ, p. 1585-1590.

[DUNN]

Dunn, Robert. Software Defect Removal, McGraw-Hill, Inc., 1984.

[EWICS3]

Bishop, P. G. (ed.). Dependability of Critical Computer Systems 3 - Techniques Directorv, The European Workshop on Industrial Computer Systems Technical Committee 7

(EWICS TC7), Elsevier Science Publishers Ltd, 1990.

[PAGAN]

Pagan, M. E., "Design and Code Inspections to Reduce Errors in Program Development,"

IBM Systems Journal , Volume 15, Number 3, 1976.

[FLORAC]

CMU/SEI-92-TR-ZZZ, "Software Quality Measurement: A Framework for Counting

Problems, Failures, and Faults," William Florae, The Quality Subgroup of the Software

Metrics Definition Working Group and the Software Process Measurement Project Team,

Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, Draft, 1992.

[FREEDMAN]

Freedman, David, Robert Pisani, and Roger Purves, "Statistics," W.W. Norton &

Company, Inc., New York, 1978.

[GRADY]

Grady, Robert B. and Caswell, Deborah, Software Metrics: Establishing a Company-Wide

Program, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1987.

7-2
[GREEN]

Green, Scott E. and Rose Pajerski, "Oeanroom Process Evolution in the SEL,"

Proceedings of the Sixteenth Annual Software Engineering Workshop, National

Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD

20771, December 1991.

[IEC65A94]

lEC 65A(Secretariat)94, "Draft British Standard 0000: Software for Computers in the

Application of Industrial Safety-Related Systems," WG9, December 6, 1989.

[IEC65A122]

lEC 65A(Secretariat)122, "Software for Computers in the Application of Industrial SafetyRelated Systems," WG9, Version 1.0, September 26, 1991.

[lEEEGLOSS]

ANSIAEEE Std 610. 12, "IEEE Standard Glossary of Software Engineering Terminology,"

The Institute of Electrical and Electronics Engineers, February, 1991.

[IEEEP1044]

IEEE P1044, "Draft Standard of: A Standard Classification for Software Errors, Faults,

and Failures," The Institute of Electrical and Electronics Engineers, August 1991.

[IEEE982.2]

ANSI/IEEE Std 982.2-1988, "Guide for the Use of IEEE Standard Dictionary of Measures

to Produce Reliable Software," The Institute of Electrical and Electronics Engineers, June,

1989.

[IEEE1012]

ANSI/IEEE Std 1012-1986, "IEEE Standard for Software Verification and Validation

Plans," The Institute of Electrical and Electronics Engineers, Inc., November 14, 1986.

[JURAN]

Juran, J. M. (ed.), Juran's Quality Control Handbook, 4th ed., McGraw-Hill, Inc., New

York, 1988.

[KELLY]

Kelly, John C, Joseph S. Sherif, and Jonathan Hops, "An Analysis of Defect Densities

Found During Software Inspections," Proceedings of the Fifteenth Annual Software

Engineering Workshop, National Aeronautics and Space Administration, Goddard Space

Flight Center, Greenbelt, MD 20771, November, 1990.

[KITCHENHAM]

Kitchenham, B. A. and B. Littlewood, Measurement for Software Control and Assurance, Elsevier Science Publishers Ltd, London and New York, 1989.

7-3
[LYLE]

Lyle, Jim, "Program Slicing," to be published in Encyclopedia of Software Engineering, John Wiley Publishing Co., New York, New York.

[LYU]

Lyu, Michael and Allen Nikora, "Applying Reliability Models More Effectively," IEEE

Software, Vol. 9., No. 4, July, 1992.

[MAKOWSKY]

Makowsky, Lawrence C, Technical Report, USA-BRDEC-TR//2516, "A Guide to Independent Verification and Validation of Computer Software," United States Army,

Belvoir Research, Development and Engineering Center, June 1992.

[MILLER]

Miller, Keith W., et al, "Estimating the Probability of Failure When Testing Reveals No

Failures," IEEE Transactions on Software Engineering, Vol.18, No.l, January 1992.

[MILLS]

Mills, H. D., M. Dyer, and R. C. Linger, "Cleanroom Software Engineering," IEEE

Software, September, 1987, pp. 19-25.

tMOD55]

Interim Defence Standard 00-55, "The Procurement of Safety Critical Software in Defence

Equipment," Parts 1 and 2, Ministry of Defence, UK, April 5, 1991.

[MYERS]

Myers, Glenford J., The Art of Software Testing, John Wiley & Sons, New York, 1979.

[NBS93]

Powell, Patricia B., NBS Special Publication 500-93, "Software Validation, Verification,

and Tesdng Technique and Tool Reference Guide," U.S. Department of

Commerce/Nadonal Bureau of Standards (U.S.), September 1982.

[NIST187]

NIST SPEC PUB 500-187, "Application Portability Profile (APP) The U.S. Government's

Open System Environment Profile OSE/1 Version 1.0," U.S. Department of

Commerce/Nadonal Institute of Standards and Technology, April 1991.

[NIST204]

Wallace, D.R., L.M. Ippolito, D.R. Kuhn, NIST SPEC PUB 500-204, "High Integrity

Software Standards and Guidelines," U.S. Department of Commerce/Nadonal Insdtute of

Standards and Technology, September, 1992.

7-4
[NISTGCR]

Craigen, Dan, Susan Gerhart, Ted Ralston, NISTGCR 93/626, "An International Survey

of Industrial Applications of Formal Methods," Volumes 1 and 2, U.S. Department of

Commerce/National Institute of Standards and Technology, March, 1993.

[NISTIR]

Wallace, D.R., W.W. Peng, L.M. Ippolito, NISTIR 4909,"Software Quality Assurance:

Documentation and Reviews," U.S. Department of Commerce/National Institute of

Standards and Technology, 1992.

[NUREG]

Wallace, D.R., L.M. Ippolito, D.R. Kuhn, NUREG/CR-5930, "High Integrity Software

Standards and Guidelines," U.S. Nuclear Regulatory Commission, September, 1992.

[OPMC]

The Organizational Process Management Cycle Programmed Workbook, Interaction

Research Institute, Inc., Fairfax, Virginia.

[PARK]

Park, Robert, CMU/SEI-92-TR-20, ESC-TR-92-20, "Software Size Measurement: A

Framework for Counting Source Statements," Software Engineering Institute, Carnegie

Mellon University, September, 1992.

[PEYTON]

Peyton and Hess, "Software Sneak Analysis," IEEE Seventh Annual Conference of the

Engineering in Medicine and Biology Society, The Institute of Electrical and Electronics

Engineers, 1985.

[PUTNAM]

Putnam, Lawrence H. and Ware Myers, Measures for Excellence, Reliable Software On

Time, Within Budget, Prentice-Hall, Inc., Englewood Cliffs, NJ, 1992.

[RIFKIN]

Rifkin, Stan and Charles Cox, "Measurement in Practice," Technical Report, CMU/SEI91-TR-16, ESD-TR-91-16, Carnegie Mellon University, 1991.

[ROME]

luorno, Rocco and Robert Vienneau, "Software Measurement Models," Draft, Prepared

for Rome Air Development Center, Griffiss Air Force Base, NY, July 1987.

[ROOK]

Rook, Paul, Software Reliability Handbook, Elsevier Science Publishers Ltd, Crown

House, London and New York, 1990.

7-5
[SCHULMEYER]

Schulmeyer, G. Gordon. Zero Defect Software, McGraw-Hill, Inc., 1990.

[SHOOMAN]

Shooman, Martin L., "A Class of Exponential Software Reliability Models," Workshop

on Software Reliability, IEEE Computer Society Technical Committee on Software

Reliability Engineering, Washington, DC, April 13, 1990.

[SQE]

"Software Measurement," Seminar Notebook, Version 1.2, Software Quality Engineering,

1991.

[SMITH]

Smith, Gary, Stadstical Reasoning, Allyn and Bacon, Boston, MA, 1991.

[STSC]

MACOIO/STSC, "Software Management Guide," Software Technology Center, Hill AFB,

UT 84-56, October, 1990.

[ROME]

Proceedings of the 3rd Annual Software Quality Workshop; Alexandria Bay, New York:

August 11-15, 1991 , Rome Laboratory, Griffiss AFB, New York.

[VOAS]

Voas, Jeffrey M. and Keith W. Miller," A Model for Improving the Testing of Reusable

Software Components," 10th Pacific Northwest Software Quality Conference, October 19-

21, 1992, Portland, Oregon.

[WALLACE]

Wallace, Dolores R. "Software Verification and Validation," to be published in Encyclopedia of Software Engineering, John Wiley Publishing Co., New York, NY.

[WALLACEFUJII]

Wallace, Dolores R. and Roger U. Fujii, "Verificadon and Validation: Techniques to Assure Reliability," IEEE Software, May 1989.

[ZAGE]

Zage, Wayne M. "Code Metrics and Design Metrics; An ACM Professional Development

Seminar," November 19, 1991.

7-6
APPENDIX A. ERROR DETECTION TECHNIQUES

A.l. Algorithm Analysis

Description:

The purpose is to determine the correctness of an algorithm with respect to its intended use, to determine its operational characteristics, or to understand it more fully in order to modify,

simplify, or improve. The analysis involves rederiving equations or evaluadng the suitability of

specific numerical techniques. Algorithms are analyzed for correctness, efficiency (in terms of

time and space needed), simplicity, opdmaUty, and accuracy. Algorithm analysis also examines

truncation and round-off effects, numerical precision of word storage and variables (e.g., singlevs. extended-precision arithmedc), and data typing influences.

Advantages:

• Effecdve and useful in general

Disadvantages:

• A particular analysis depends on the pardcular model of computadon (e.g, Turing

machine, random access machine). If the assumptions of the model are inappropriate,

then the analysis will be inaccurate.

Type of Errors Detected:

• Incorrect, inappropriate, or unstable algorithms

• Program does not terminate

• Inability to operate on the full range of data - e.g., trigonometric routine only works in

the first quadrant

• Incorrect analysis of computational error (effect of round-off and truncation errors)

• Incompatible data representations - e.g., input in lbs., program processes kilograms

• Incompatibility with hardware or software resources

References: [IEEE1012], [DUNN], [WALLACE], [NBS93]

A.2. Back-to-Back Testing

Description:

This technique is used to detect test failures by comparing the output of two or more programs

implemented to the same specification. The same input data is applied to two or more program

versions and their outputs are compared to detect anomalies. Any test data selection strategy can be used for this type of testing, although random testing is well suited to this approach. Also

known as comparison testing.

Advantages:

Permits a large number of tests to be made with little effort

A-1
Rapid fault detection

Disadvantages:

• Requires the construction of at least one secondary program, although this may be

available as part of the overall development

• Discrepancies must be analyzed manually to determine which program is at fault (it is not

sufficient to assume that majority is always correct)

Types of Errors Detected:

• Does not detect specific errors, only anomalies or discrepancies between programs

References: [EWICS3]

A.3. Boundary Value Analysis

Description:

The purpose is to detect and remove errors occurring at parameter hmits or boundaries. The

input domain of the program is divided into a number of input classes. The tests should cover

the boundaries and extremes of the classes. The tests check that the boundaries of the input

domain of the specification coincide with those in the program. The value zero, whether used

directly or indirectly, should be used with special attention (e.g., division by zero, null matrix, zero table entry). Usually, boundary values of the input produce boundary values for the output.

Test cases should also be designed to force the output to its extreme values. If possible, a test case which causes output to exceed the specification boundary values should be specified. If output is a sequence of data, special attention should be given to the first and last elements and

to lists containing zero, one, and two elements.

Advantages:

• Verifies that program will behave correctly for any permissible input or output

Disadvantages:

• No significant disadvantages in itself but for programs with many types of input cannot

test all combinations of input and therefore cannot identify problems resulting from

unexpected relationships between input types

Types of Errors Detected

• Algorithm errors

• Array size

• Specification errors

References: [MYERS]

A-2
A.4. Control Flow Analysis/Diagrams

Description:

This technique is most applicable to real time and data driven systems. Logic and data

requirements are transformed from text into graphic flows, which are easier to analyze.

Examples of control flow diagrams include PERT, state transition, and transaction diagrams. For

large projects, control flow analysis using control flow diagrams that show the hierarchy of main

routines and subfunctions are useful to understand the flow of program control. Purpose is to detect poor and potentially incorrect program structures. The program is represented by a directed graph, which is analyzed for the errors below.

Advantages:

• Simple to apply

• Readily automated

Disadvantages:

• Results require some interpretation. Identified anomalies may not be faults.

• Sometimes difficult to deal with "ahasing" where different variables are associated with

the same locations in memory

Types of Errors Detected:

• Inaccessible/unreachable code

• Knotted code - If code is well-structured the directed graph can be reduced to a single

node. If code is poorly structured, it can only be reduced to a "knot" composed of several

nodes.

References: [IEEE 10 12], [EWICS3]

A.5. Database Analysis

Description:

Database analysis is performed on programs with significant data storage to ensure that common

data and variable regions are used consistently between all call routines; data integrity is enforced

and no data or variable can be accidentally overwritten by overflowing data tables; data access through indirect access is checked; and data typing and use are consistent throughout all program

elements. Useful for programs that store program logic in data parameters. The purpose is to ensure that the database structure and access methods are compatible with the logical design.

Diagrams are useful for understanding user privileges.

Advantages:

Supports interface analysis

A-3
Disadvantages:

• May require manual examination of diagrams for access problems

Types of Errors Detected:

• Inconsistent use of data types

• Incorrect access protections

References: [IEEE 10 12]

A.6. Data Flow Analysis

Description:

The purpose is to detect poor and potentially incorrect program structures. Data flow analysis

combines the information obtained from the control flow analysis with information about which

variables are read or written in different portions of code. May also be used in the design and

implementation phases.

Advantages:

• Readily automated -

• Easy to apply

Disadvantages:

• Requires some interpretation

• Some anomalies may not be faults

Types of Errors Detected:

Undefined input/output data or format

• Incorrect data flow

• Variables that are read before they are written (likely to be an error, and is bad

programming practice)

• Omitted code - indicated by variables that are written more than once without being read

• Redundant code - indicated by variables that are written but never read

References: [EWICS3], [IEEE1012]

A.7. Data Flow Diagrams

Description:

Data flow diagrams are used to describe the data flow through a program in a diagrammatic

form. They show how data input is transformed to output, with each stage representing a

distinct transformation. The diagrams use three types of components:

A-4
1. Annotated bubbles - bubbles represent transformation centers and the annotation

specifies the transformation

2. Annotated arrows - arrows represent the data flow in and out of the transformation

centers, annotations specify what the data is 3. Operators (AND, XOR) - used to link the annotated arrows

Data flow diagrams only describe data, and should not include control or sequencing information.

Each bubble can be considered a black box which, as soon as its inputs are available, transforms

them to outputs. Each should represent a distinct transformation, whose output is somehow

different from its input. There are no rules regarding the overall structure of the diagram.

Advantages:

• They show transformations without making assumptions about how the transformations

are implemented.

Disadvantages:

• Inability to provide information about the transformation process

Type of Errors Detected:

• Incorrect data input/output

• Inconsistencies in data usage

References: [IEC65A94]

A.8. Decision Tables (Truth Tables)

Description:

The purpose is to provide a clear and coherent analysis of complex logical combinations and

reladonships. This method uses two-dimensional tables to concisely describe logical relationships

between boolean program variables.

Advantages:

Their conciseness and tabular nature enable the analysis of complex logical combinadons

expressed in code.

• Potendally executable if used as specificadons

Disadvantages:

Tedious effort required

Types of Errors Detected:

• Logic

References: [IEC65A122]

A-5
A.9. Desk Checking (Code Reading)

Description:

Code is read by an expert, other than the author of the code, who performs any of the following:

looking over the code for obvious defects, checking for correct procedure interfaces, reading the

comments to develop a sense of what the code does and then comparing it to its external

specifications, comparing comments to design documentation, comparing comments to design

documentation, stepping through with input conditions contrived to "exercise" all paths including

those not direcdy related to the external specifications, checking for compliance with

programming standards and conventions, any combination of the above.

Advantages:

Inexpensive

• Capable of catching 30% of all errors, if performed meticulously

• Can be more effective than functional testing or structural testing (NASA Goddard Space

Flight Center experiment, see [BASILI])

Disadvantages:

• Requires enormous amount of discipline

• Few people are able to use this technique effectively

• Usually less effective than walkthroughs or inspections

Types of errors detected:

LOGIC AND CONTROL

• unreachable code

• improper nesting of loops and branches

• inverted predicates

• incomplete predicates

• improper sequencing of processes

• infinite loops

• instruction modification

• failure to save or restore registers

• unauthorized recursion

• missing labels or code

• unreferenced labels

COMPUTATIONAL

• missing validity tests

• incorrect access of array components

• mismatched parameter lists

• initialization faults

• anachronistic data

• undefined variables

• undeclared variables

A-6
misuse of variables (locally and globally)

data fields unconstrained by natural or defined data boundaries

inefficient data transport

OTHER

calls to subprograms that do not exist

improper program linkages

input-output faults

prodigal programming

failure to implement the design

References: [WALLACE], [DUNN], [BEIZER], [BASILI]

A.IO. Error Seeding

Description:

The purpose of this technique is to determine whether a set of test cases is adequate. Some

known error types are inserted into the program, and the program is executed with the test cases under test conditions. If only some of the seeded errors are found, the test case set is not

adequate. The ratio of found seeded errors to the total number of seeded errors is approximate

equal to the ratio of found real errors to total number of errors, or

In the equation, one can solve for the total number of real errors, since the values of the other

three are known. Then, one can estimate the number of errors remaining by subtracting the

number of real errors found from the total number of real errors. The remaining test effort can

then be estimated. If all the seeded errors are found, this indicates that either the test case set

is adequate, or that the seeded errors were too easy to find.

Advantages:

• Provides indication that test cases are structured adequately to locate errors.

Disadvantages:

• For this method to be valid and useful, the error types and the seeding positions must

reflect the statistical distribution of real errors.

Types of Errors Detected:

• Does not detect errors, but determines adequacy of test set

Number of seeded

errors found

Total number of

seeded errors

Number of real errors found

Total number of

real errors

References: [IEC65A122]

A-7
A.ll. Finite State Machines

Description:

The purpose is to define or implement the control structure of a system. Many systems can be

defined in terms of their states, inputs, and actions. For example, a system is in state Sj, receives an input I, then carries out action A, and moves to state S2. By defining a system's actions for

every input in every state we can completely define a system. The resulting model of the system

is a finite state machine (FSM). It is often drawn as a state transition diagram, which shows how

the system moves from one state to another, or as a matrix in which the dimensions are state and

input. Each matrix entry is identified by a state and input, and it specifies the action and new

state resulting from receipt of the input in the given state.

Advantages:

• Allows important properties to be checked mechanically and reliably.

• Simple to work with .

Disadvantages:

None that are major

Types of Errors Detected:

• Incomplete requirements specification - check that there is an action and

new state for every input in every state

• Inconsistent requirements - check that only one state change is defined for each state and

input pair

References: [EWICS3]

A.12. Formal Methods (Formal Verification, Proof of Correctness, Formal

Proof of Program)

Description:

The purpose is to check whether software fulfills its intended funcdon. Involves the use of

theoredcal and mathematical models to prove the correctness of a program without execudng it. The requirements should be written in a formal specification language (e.g., VDM, Z) so that

these requirements can then be verified using a proof of correctness. Using this method, the

program is represented by a theorem and is proved with first-order predicate calculus. A number

of assertions are stated at various locations in the program, and are used as pre and post

conditions to various paths in the program. The proof consists of showing that the program

transfers the preconditions into the postconditions according to a set of logical rules, and that the

program terminates.

Advantages:

• Allows for rigorous statement concerning correctness

A-8
Possibly the only way of showing the correctness of general WHILE loops

Disadvantages:

• Time consuming to do manually

• Requires enormous amount of intellectual effort

• Must be checked mechanically for human errors

• Difficult to apply to large software systems

• If formal specifications are not used in the design, then formal verification (proof of

correctness after implementation tends to be extremely difficult

• Only applicable to sequential programs, not concurrent program interactions

References: [IEC65A122], [ROOK]

A.13. Information Flow Analysis

Description:

An extension of data flow analysis, in which the actual data flows (both within and between

procedures) are compared with the design intent. Normally implemented with an automated tool

where the intended data flows are defined using a structured comment that can be read by the

tool.

Advantages:

• Simple to apply

• Readily automated

Disadvantages:

• Results require some interpretation

• Some anomalies may not be faults

Types of Errors Detected:

Undefined input / output data or format

• Incorrect flow of information

References: [EWICS3]

A.14. (Fagan) Inspections

Description:

An inspection is an evaluation technique in which software requirements, design, code, or other

products are examined by a person or group other than the author to detect faults, violations of

development standards, and other problems. An inspection begins with the distribution of the

item to be inspected (e.g., a specification, some code, test data). Each participant is required to analyze the item on his own. During the inspection, which is the meeting of all the participants.

A-9
the item is jointly analyzed to find as many errors as possible. All errors found are recorded, but no attempt is made to correct the errors at that time. However, at some point in the future, it must be verified that the errors found have actually been corrected. Inspections may also be

performed in the design and implementation phases.

Advantages:

• Provides comprehensive statistics on classes of errors

• Studies have shown that inspections are an effective method of increasing product quality

(e.g., reliability, usability, maintainability)

• Effective for projects of all sizes

•

' Qualitative benefits: less complex programs, subprograms written in a consistent style,

highly visible system development, more reliable esdmating and scheduling, increased

user sadsfaction, improved documentadon, less dependence on key personnel for criucal

skills

Disadvantages:

• Inspectors must be independent of programmers

• Programmers may feel inspection is a personal attack on their work

• Time consuming, involving several staff (2 or 3 pages of not-difficult code may take 3

h to inspect)

Type of Errors Detected:

• Weak modularity

• Failure to handle exceptions

• Inexpansible control structure

• Nonexisdng or inadequate error traps

• Incomplete requirements

• Infeasible requirements

• Conflicting requirements

• Incorrect specificadon of resources

References: [DUNN], [FAGAN], [IEC65A94], [MYERS], [NBS93]

A.15. Interface Analysis

Description:

This technique is used to demonstrate that the interfaces of subprograms do not contain any

errors or any errors that lead to failures in a pardcular applicadon of the software or to detect all errors that may be relevant. Interface analysis is especially important if interfaces do not contain

assertions that detect incorrect parameter values. It is also important after new configuradons

of pre-existing subprograms have been generated. The types of interfaces that are analyzed

include external, internal, hardware/hardware, software/software, software/hardware, and

software/database. Interface analysis may include the following:

A-10
• Analysis of all interface variables at their extreme positions

Analysis of interface variables individually at their extreme values with other interface

variables at normal values

• Analysis of all values of the domain of each interface variable with other interface

variables at normal values

Advantages:

• Can locate problems that would prevent system from functioning due to improper

interfaces

• Especially useful for software requirements verification and design verification

• When used with prototyping or simulation, can find many critical errors that would be

cosdy to correct in the delivered system

• Software design tools exist for analysis of interfaces, during design phase

Disadvantages:

• Manual effort may be time-consuming

Types of Errors detected:

• Input / output description errors (e.g., values of input variables altered)

• Actual and formal parameters mismatch (precision, type, units, and number)

• Incorrect functions used or incorrect subroutine called

• Inconsistency of attributes of global variables (common, etc.)

• Incorrect assumptions about static and dynamic storage of values (i.e., whether local

variables are saved between subroudne calls)

• Inconsistencies between subroudne usage list and called subroutine

References: [WALLACE], [MAKOWSKY]

A.16. Interface Testing

Description:

Similar to interface analysis, except test cases are built with data that tests all interfaces. Interface

tesdng may include the following:

• Testing all interface variables at their extreme posidons

Tesdng interface variables individually at their extreme values with other interface

variables at normal values

• Tesdng all values of the domain of each interface variable with other interface variables

at normal values

Tesdng all values of all variables in combination (may be feasible only for small

interfaces).

A-11
Advantages:

Locates errors that may prevent system from operating at all or locates errors in timing

of interface responses (e.g., slow system response to users was a factor in the failure of

the THERAC medical system)

Disadvantages:

Without automation of design, or code modules, manual searching for interface

parameters in all design or code modules can be time consuming

Types of Errors Detected:

• Input / output description errors

• Inconsistent interface parameters

References: [IEC65A122]

A«17. Mutation Analysis

Description:

The purpose is to determine the thoroughness with which a program has been tested, and in the

process, detect errors. This procedure involves producing a large set of versions or "mutations"

of the original program, each derived by altering a single element of the program (e.g., changing

an operator, variable, or constant). Each mutant is then tested with a given collection of test data

sets. Since each mutant is essentially different from the original, the testing should demonstrate

that each is in fact different. If each of the outputs produced by the mutants differ from the

output produced by the original program and from each other, then the program is considered

adequately tested and correct.

Advantages:

Applicable to any algorithmic solution specification

Results are good predictors of operational reliability

Disadvantages:

Likely to require significant amounts of human analyst time and good insight

• Requires good automated tools to be effective

Reliable only if all possible mutation errors are examined

• Cannot assure the absence of errors which cannot be modeled as mutations

Type of Errors Detected:

Any errors that can be found by test

References: [ANS104], [NBS93], [DEMILLO]

A-12
A.18. Performance Testing

Description:

The purpose is to measure how well the software system executes according to its required

response times, cpu usage, and other quantified features in operation. These measurements may

be simple to make (e.g., measuring process time relative to volumes of input data) or more

complicated (e.g., instrumenting the code to measure time per function execution).

Advantages:

• Useful for checking timing synchronization of functions, memory locations, memory

requirements and other performance features.

Disadvantages:

• Caution required in instrumenting code to ensure the instrumentation itself does not

interfere with processing of functions or with locations of bytes under examination

Types of Errors Detected:

• Timing, synchronization, and memory allocation

References: [ROOK]

A.19. Prototyping / Animation

Description:

Purpose is to check the feasibility of implementing a system against the given constraints and to communicate the specifier's interpretation of the system to the customer, in order to locate

misunderstandings. A subset of system functions, constraints, and performance requirements are

selected. A prototype is built using high level tools, is evaluated against the customer's criteria,

and the system requirements may be modified as a result of this evaluation.

Advantages:

• Better communication with customer

• Early detection of problems

• Check feasibility of new ideas or techniques

Disadvantages:

• Unnecessary and expensive if problem is well understood

• Tools are needed for quick implementation

Types of Errors Detected:

• User related

• Interface related

• Omitted functions

A-13
• Undesired functions

• Poorly defined functionality (e.g., specifications that do not cover all expected cases)

• Errors in the specification that lead to inconsistent states, failure conditions, erroneous

results

• Contradictions in requirements

• Impossibility, infeasibility of requirements

A.20. Regression Analysis and Testing

Description:

Regression analysis is used to reevaluate requirements and design issues whenever any significant

code change is made. It involves retesting of a software product to detect faults made during

modification, to verify that modification has not caused unintended side effects, and to verify that

the modified software still meets its specified requirements. Any changes made during

installation and test are reviewed using regression analysis and test to verify that the basic

requirements and design assumptions, which affect other areas of the program, have not been

violated.

Advantages:

• Effectiveness depends on the quality of the data used. If tests based on the functional

requirements are used to create the test data, technique is highly effective

Disadvantages:

Expense can appear to be prohibitive, especially for small changes. However, it can often

be determined which functions may be affected by a given change, so that the amount of

testing can be reduced in these cases

Types of Errors Detected:

• Errors caused by system modifications or corrections

References: [ANS104], [WALLACE], [IEEE1012], [NBS93]

A.21. Requirements Parsing

Description:

This technique involves the examination of individual requirements statements to ensure that each

statement is complete, readable, accurate, and consistent with other requirements. The manual

technique requires analysis of the attributes of a statement: initiator of action, the action, the

object of the action, conditions (e.g., when positive state reached), constraints (limits), source of

action, destination (e.g., printer, screen, plotter), mechanism, reason for the action. When the

data from a set of requirements is examined collectively, the results of the analysis may aid in

identifying trends.

A-14
Advantages:

• Promotes clarity, correctness, completeness, testability, and accuracy

• Can help to establish an English base from which to write a formal specification

Disadvantages:

• Very narrow look at each requirement. Could detract from analysis of more global

examination of how the requirements fit together.

Types of errors detected:

• Inconsistency in requirements

• Incomplete requirement

• Untestable requirement

A.22. Reviews

Description:

A review is a meeting at which the requirements, design, code, or other products of a software

development project are presented to the user, sponsor, or other interested parties for comment

and approval, often as a prerequisite for concluding a given phase of the software development

process. Usually held at end of a phase, but may be called when problems arise. Often referred

to as "Formal Review" versus desktop review of materials.

Advantages:

• Provides opportunity to change course of a project before start of next phase.

• Because scope of review is usually broad, gives opportunity to recognize global problems

Disadvantages:

• If participants do not have materials ahead of time and spend time preparing, review will

accomplish little or nothing.

• Attention focus on major issues

References: [ANS104], [IEEE 10281

A.23. Sensitivity Analysis

Description:

Sensitivity analysis is a new method of quantifying ultra-reliable software during the

implementation phase. It is based on the fault-failure model of software and attempts to approximate this model. There are three necessary conditions:

1. The fault must be executed.

2. The fault must affect the computational data state directly after the fault location.

3. The affected data state must propagate to an output variable.

A-15
Sensitivity analysis is based on the premise that software testability can predict the probability

that failure will occur when a fault exists given a particular input distribution. A sensitive

location is one in which faults cannot hide during testing. The approach expects reasonably

"close to correct" code; results are tied to selected input distribution. The internal states are perturbed to determine sensitivity. Researchers of this technique have developed a tool that

performs several analyses on source code to esdmate the testability and sensitivity of the code.

These analyses require instrumentadon of the code and produce a count of the total execudons

through an operation (execution analysis), an infection rate estimate, and a propagation analysis.

Advantages:

• While software testing provides some quantification of reliability, sensitivity analysis

provides quantification of software testing.

• Presents a different approach to software reliability assessment, which usually is based on an exponential distribution of remaining faults over time.

• Promising research that those needing ultra-reliable software should continue to follow.

Disadvantages:

• Intended to operate on code that has already been formally verified

• Still new, so that effectiveness of this technique is not yet known and use is not yet

widespread

• Developed and currentiy promoted by only one company

• Tool is not sold, but rather the service of operating the tool on a client's code is sold

Types of Errors Detected:

• None, but is an aid to error detection techniques

References: [MILLER], [VOAS]

A.24. Simulation

Description:

Simulation is used to test the functions of a software system, together with its interface to the

real environment, without modifying the environment in any way. The simulation may be

software only or a combination of hardware and software. Simulation is used to evaluate the

interactions of large, complex systems with many hardware, user, and other interfaces. A model

of the system to be controlled by the actual system under test is created. This model mimics the

behavior of the controlled system, and is for testing purposes only. It must provide all inputs

of the system under test which will exist when that system is installed; respond to outputs from

the system in a way which accurately represents the controlled system; have provision for

operator inputs to provide any perturbations with which the system under test is required to cope.

When software is being tested, the simulation may be a simulation of the target hardware with

its inputs and outputs. In the installation phase, it is used to test operator procedures and to help

isolate installation problems.

A-16
Advantages:

Enables simulation of critical aspects of the software that would not be practical to analyze manually.

Provides means of performing functional tests of the system's behavior in the event of

catastrophes which could not otherwise not be tested

• Can provide a means of achieving "long term" test in a short period

• Can provide a means of investigating behavior at critical points by slowing the timescale or single stepping

Disadvantages:

• Difficult to achieve independently of the software being tested

• May require considerable resource both to create the simulation and to operate it

• Dependent on a model of the system which itself could contain safety related flaws

Types of Errors Detected:

• Interface errors

• Logical errors

• Errors in performance

References: [IEEE 10 12], [WALLACE], [IEC65A122], [IEC65A94]

A.25. Sizing and Timing Analysis

Description:

Sizing/timing analysis is performed during incremental code development by obtaining program

sizing and execution timing values to determine if the program will satisfy processor size and

performance requirements allocated to the software. Significant deviations between actual and

predicted values is a possible indication of problems or the need for additional examination. This

technique is most applicable to real-time programs having response time requirements and

constrained memory execution space requirements.

Advantages:

• Opportunity to study sequence of operations, especially interfaces between humans and

response to user commands

• Indicator of whether integrated system will perform appropriately

Disadvantages:

• Time consuming when conducted by poring over control flow or data flow diagrams

• When timing results are collected by executing code, must be careful that test code does

not alter timing as it would be without the test code

Types of Error Detected:

• Unacceptable processor load

• Control structure ignores processing priorities

A-17
References: [WALLACE], [DUNN], [ffiEE1012]

A.26. Slicing

Description:

Slicing is a program decomposition technique that is based on extracting statements relevant to a specific computation from a program. It produces a smaller program that is a subset of the

original program. Without intervening irrelevant statements, it is easier for a programmer

interested in a subset of the program's behavior to understand the corresponding slice rather than

to deal with the entire program. This technique can be applied to program debugging, testing,

parallel program execution and software maintenance. Several variadons of this technique have

been developed, including program dicing, dynamic slicing and decomposidon slicing.

Advantages:

• Readily automated

• , Reduces time needed for debugging and testing

Disadvantages:

• Resource consuming tool/ method

Types of Errors Detected:

Aids in finding root of errors during debugging and tesdng, by narrowing the focus of

invesdgadon

References: [LYLE]

A.27. Software Sneak Circuit Analysis

Description:

This technique is used to detect an unexpected path or logic flow which causes undesired

program funcdons or inhibits desired functions. Sneak circuit paths are latent condidons

inadvertendy designed or coded into a system, which can cause it to malfunction under certain

condidons.

To perform sneak circuit analysis, source code is first converted, usually by computer, into an

input language description fde into topological network trees. Then the trees are examined to idendfy which of the six basic topological patterns appear in the trees. Analysis takes place

using checklists of questions about the use and relationships between the basic topological

components.

Advantages:

• Effecdve in finding errors not usually detected by desk checking or standard V&V

techniques

A-18
• Applicable to programs written in any language

• Applicable to hardware, software, and the combined system

Disadvantages:

• Labor intensive

• Likely to be performed late in the development cycle, so changes will be costly

Types of Error Detected:

• Unintended functions/outputs

References: [PEYTON], [EWICS3]

A.28. Stress Testing

Description:

Involves testing the response of the system to extreme conditions (e.g., with an exceptionally high

workload over a short span of time) to identify vulnerable points within the software and to show

that the system can withstand normal workloads. Examples of testing conditions that can be

applied include the following:

• If the size of the database plays an important role, then increase it beyond normal

conditions.

• Increase the input changes or demands per time unit beyond normal conditions.

• Tune influential factors to their maximum or minimal speed.

• For the most extreme case, put all influential factors to the boundary conditions at the

same dme.

Under these test conditions, the time behavior can be evaluated and the influence of load changes

observed. The correct dimension of internal buffers or dynamic variables, stacks, etc. can be

checked.

Advantages:

• Often the only method to determine that certain kinds of systems will be robust when

maximum numbers of users are using the system, at fastest rate possible (e.g., transaction

processing) and to identify that contingency acdons planned when more than maximum

allowable numbers of users attempt to use system, when volume is greater than allowable

amount, etc.

Disadvantages:

Requires large resources

Types of Errors Detected:

• Design errors related to full-service requirements of system and errors in planning defaults

when system is over-stressed

A-19
References: [MYERS]

A.29. Symbolic Execution

Description:

This is an evaluation technique in which program execution is simulated using symbols rather

than actual values for input data, and program output is expressed as logical or mathematical

expressions involving these symbols.

Advantages:

• No input data values are needed

• Results can be used in program proving

• Useful for discovering a variety of errors

Disadvantages:

• Result will consist of algebraic expressions which easily get very bulky and difficult to interpret

• Difficult to analyze loops with variable length

• For most programs, the number of possible symbolic expressions is excessively large

• Unlikely to detect missing paths

Studies have shown that in general, it is not more effective than the combined use of

other methods such as static and dynamic analyses

Types of Errors Detected:

• None, but is an aid for detecting errors. A person must verify the correctness of the

output generated by symbolic execution in the same way that output is verified when

generated by executing a program over actual values.

References: [ANS104], [EWICS3], [NBS93]

A.30. Test Certification

Description:

The purpose is to ensure that reported test results are the actual finding of the tests. Test related

tools, media, and documentation shall be certified to ensure maintainability and repeatability of

tests. This technique is also used to show that the delivered software product is idendcal to the

software product that was subjected to V&V.

Advantages:

• Assurance that the test results are accurately presented

• Assurance that the corrected version of product is in compliance with test findings

Disadvantages:

A-20
• Often mistaken as a certification of system quality

Type of Errors Detected:

• Incorrect test results reported

• Tests reported that never occurred

• Incorrect version of product shipped

References: [IEEE 101 2]

A.31. Traceability Analysis (Tracing)

Description:

There are several types of traceability analysis, including requirements trace, design trace, code

trace, and test trace. Traceability analysis is the process of verifying that each specified

requirement has been implemented in the design / code, that all aspects of the design / code have

their basis in the specified requirements, and that testing produces results compatible with the

specified requirements.

Advantages:

Highly effective for detecting errors during design and implementation phases

• Valuable aid in verifying completeness, consistency, and testability of software

• Aids in retesting software when a system requirement has been changed

Disadvantages:

• No significant disadvantages

Types of Errors Detected:

REQUIREMENTS:

• Omitted functions

• Higher-order requirement improperly translated

• Software specification incompatible with other system specifications

DESIGN:

Omission or misinterpretation of specified requirements

• Detailed design does not conform to top-level design

• Failure to conform to standards

CODE:

• Omission or misinterpretation of specified requirements

• Code does not conform to detailed design

• Failure to conform to standards

TEST:

A-21
• Software does not perform functions and produce outputs in conformance with the

requirements specification

References: [DUNN], [ANS104], [NBS93]

A.32. Walkthroughs

Description:

A walkthrough is an evaluation technique in which a designer or programmer leads one or more

other members of the development team through a segment of design or code, while the other

members ask questions and make comments about technique, style, and identify possible errors, violations of development standards, and other problems. Walkthroughs are similar to reviews,

but are less formal. Other essential differences include the following:

• Participants are fellow programmers rather than representadves of other funcdons

• . Frequendy no preparadon

• Scope - standards usually ignored. Successful stadc analysis results generally not

confirmed

• Checklists are rarely used

• Follow-up is often ignored

Advantages:

• Less intimidadng than formal reviews

• Idendfies the most error-prone sections of the program, so more attendon can be paid to these sections during testing

• Very effective in finding logic design and coding errors

Disadvantages:

Designers or programmers may feel walkthrough is an attack on their character or work

Types of Errors Detected:

• Interface

• Logic

Data

• Syntax

References: [ANS104], [DUNN], [IEC65A94]

A-22
APPENDIX B. ERROR ANALYSIS TECHNIQUES CITED IN SOFTWARE STANDARDS

For [NUREG, NIST204], NIST conducted a survey of software engineering standards and

guidelines used in the assurance of the quality of high integrity systems. Many of those

documents require or recommend use of software quality assurance and software verification and

validation techniques to locate errors in all phases of the software lifecycle. However, these

techniques may not be sufficient to detect all errors. NIST extended that study to include other

standards and guidelines in order to determine the extent of recommendations, requirements, and

guidance for error analysis techniques. The results of this study are presented here.

This study included the examination of approximately 50 documents, which include software

standards, draft standards, and guidelines, all selected from a bibliographic search. These

documents pertain to high integrity systems, such as safety systems for nuclear power plants,

message transmitting devices, medical devices, and other safety-critical software. The list of

documents that were reviewed, with the full document titles and corresponding identifiers, are shown in Table B-1.

One objective of this study of standards was to determine whether there is readily available

supporting literature on techniques that are required by the standards. The study showed that in

most cases, there is not adequate information available in. the standards on how to use the

techniques, and that a developer would have to rely on library searches (e.g., books, journal

articles) or information collected from previous projects. Another objective of this study was to look for consensus in the standards to determine which techniques are generally agreed upon by

experts for use.

Results showed that the standards varied in coverage of techniques. Some address techniques

in detail, while others only mention them. The study also found that some commonly used

techniques like complexity analysis were cited infrequently. Yet, technical literature and

availabihty of complexity metrics tools indicate that complexity analysis is often used to support

error detection.

The specific findings are presented in Table B-2 below. For each technique, the table specifies

the type, the standards which cite the technique, and the depth of discussion, e.g., whether the

technique is mentioned, recommended, defined, or required. Although all of the selected

documents were reviewed, information on them does not appear in the table unless they address

error analysis in some way.

Only a few techniques are explicidy required. These techniques may be required only under

certain circumstances (e.g., reviews are required only during the design phase). Many standards

only mention techniques, and do not define, recommend, or require their use. Techniques that are "mentioned" may be examples of appropriate techniques that satisfy certain criteria. A few

standards provide extensive information on several techniques, such as IEC65A(Secretariat)122

and AFISC SSH 1-1.

A-23
Several documents have appendices which are not part of the standard, but are included with the

standard for information purposes. These include ANSI/IEEE-ANS-7-4.3.2-19XX Draft 2,

ANSI/ANS-10.4-1987, and ANSI/IEEE Std 1012-1986. The appendices provide short definitions

of the selected techniques.

FIPS 101 recommends techniques that can be used in each lifecycle phase. ANSI/IEEE Std

1012-1986 provides similar information in a chart containing optional and required techniques

and specifies the lifecycle phases in which they can be used.

A-24
Table B-1. List of Reviewed Documents

IDENTIFIER NUMBER AND TITLE

AFISC SSH I-l AFISC SSH 1-1, "SoUwarc System Safety," Headquarters Air Force

Inspection and Safety Center, 5 September 1985. ANSI X9.9-I986 ANSI X9.9-1986, "Financial Institution Message Authentication

(Wholesale)," X9 Secretariat, American Bankers Association, August 15, 1986. ANSI X9.17-1985 ANSI X9.17-1985, "Financial Institution Key Management (Wholesale),"

X9 Secretariat, American Bankers Association, April 4, 1985.

ANSI/ANS-10.4-1987 ANSI/ANS-10.4-1987, "Guidelines for the Verification and Validation of

Scientific and Engineering Computer Programs for the Nuclear Industry,"

American Nuclear Society, May 13, 1987. ANSI/ASQC A3- 1987 ANSI/ASQC A3- 1987, "Quality Systems Terminology," American Society

or Quality Control, 1987.

ANSI/IEEE Std 730.1-1989 ANSI/IEEE Std 730.1-1989, "IEEE Standard for Software Quality Assurance Plans," Institute of Electrical and Elecu-onics Engineers, Inc., October 10, 1989.

ANSI/IEEE Std 1012-1986 ANSI/IEEE Std 1012-1986, "IEEE Standard for Software Verification and

Validation Plans," The Institute of Electrical and Elecu-onics Engineers,

Inc., November 14, 1986.

ANSI/IEEE-ANS-7-4.3.2-

19XX

ANSI/IEEE-ANS-7-4.3.2-1982, "Application Criteria for Programmable

Digital Computer Systems in Safety Systems of Nuclear Power Generating

Stations," American Nuclear Society, 1982. AND ANSI/IEEE-ANS-7-

4.3.2-19XX, Draft 2, as of November, 1991.

AQAP-13 AQAP-13, "NATO Software Quality Control System Requirements,"

NATO, August 1991. ASME NQA- 1-1989

Supplement 17S-1

Supplement 17S-1, ASME NQA-1-1989, "Supplementary Requirements

for Quality Assurance Records," The American Society of Mechanical

Engineers.

ASME NQA-2a-1990 ASME NQA-2a-1990, "Quality Assurance Requirements for Nuclear

Facility Applications," The American Society of Mechanical Engineers,

November 1990. ASME NQA-3-1989 ASME NQA-3-1989, "Quality Assurance Program Requirements for the Collection of Scientific and Technical Information for Site Characterization of High-Level Nuclear Waste Repositories," The

American Society of Mechanical Engineers, March 23, 1990.

CAN/CSA-Q396. 1.2-89 CAN/CSA-Q396. 1.2-89, "Quality Assurance Program for Previous

Developed Software Used in Critical Applications," Canadian Standards

Association, January 1989.

B-1
IDENTIFIER NUMBER AND TITLE

CSC-STD-003-85 CSC-STD-003-85, "Computer Security Requirements-Guidance for Applying ilie Department of Defense Trusted Computer System Evaluation

Criteria in Specific Environments," Department of Defense, 25 June 1985. DLP880 DLP880, "(DRAFT) Proposed Standard for Software for Computers in the Safety Systems of Nuclear Power Stations (based on lEC Standard 880),"

David L. Pamas, Queen's University, Kingston, Ontario, March, 1991. DOD 5200.28-STD DOD 5200.28-STD, "Department of Defense Trusted Computer System

Evaluation Criteria," Department of Defense, December 1985.

E.F.T. Message Authentication

Guidelines

Criteria and Procedures for Testing, Evaluating, and Certifying Message

Authentication Devices for Federal E.F.T. Use," United States Department

of the Treasury, September 1, 1986. FDA/HIMA (DRAFT) FDA/HIMA, "(DRAFT) Reviewer Guidance for Computer-Conu-olled

Devices," Medical Device Industry Computer Software Committee,

January 1989.

FIPS 74 . FIPS 74, "Guidelines for Implementing and Using the NBS Data

Encryption Standard," U.S. Deparunent of Commerce/National Bureau of

Standards, 1981 April 1.

FIPS 81 FIPS 81, "DES Modes of Operation," U.S. Department of Commerce/

National Bureau of Standards, 1980 December 2.

FIPS 46-1 FIPS 46-1, "Data Encryption Standard," U.S. Deparunent of Commerce/National Bureau of Standards, 1988 January 22.

FIPS 101 FIPS 101, "Guideline for Lifecycle Validation, Verification, and Testing

of Computer Software," U.S. Department of Commerce/National Bureau

of Standards, 1983 June 6.

FIPS 132 FIPS 132, "Guideline for Software Verification and Validation Plans," U.S. Department of Commerce/National Bureau of Standards, 1987

November 19.

FIPS 140 FS 1027 FIPS 140 FS 1027, "General Security Requirements for Equipment Using

the Data Encryption Standard," General Services Administration, April 14, 1982.

FIPS 140-1 FIPS 140-1, "Security Requirements for Cryptographic Modules," U.S. Department of Commerce/National Institute of Standards and Technology,

1990 May 2.

Guide to the Assessment of

Reliability

"89/977 14-Guide to the Assessment of Reliability of Systems Containing

Software," British Standards Institution, 12 September 1989.

Guideline for the Categorization of Software

"Guideline for the Categorization of Software in Ontario Hydro's Nuclear

Facilities with Respxjct to Nuclear Safety," Revision 0, Nuclear Safety

Department, June 1991.

B-2
IDENTIFIER NUMBER AND TITLE

Guidelines for Assuring

Testability (DRAFT)

"(DRAFT) Guidelines for Assuring Tcsiabiliiy," The Institution of

Electrical Engineers, May 1987.

lEC 880 lEC 880, "Software for Computers in the Safety Systems of Nuclear

Power Stations," International Electrotechnical Commission, 1986. lEC 880 Supplement 45AAVG-A3(Secretary)42, "(DRAFT) Software for Computers Important

to Safety for Nuclear Power Plants as a Supplement to lEC Publication

880," International Electrotechnical Commission Technical Committee:

Nuclear Instrumentation, Sub-Committee 45A: Reactor Instrumentation,

Working Group A3: Data Transmission and Processing Systems, May

1991.

IEC65A(Secretariat)122 IECArC65A WG9, "89/33006 DC - (DRAFT) Software for Computers in the Application of Industrial Safety-Related Systems," British Standards

Institution, 6 December 1989.

IEC65A(Secretariat)123 IEC^C65A WGIO, "89/33005 DC - (DRAFT) Functional Safety of Electrical/Electronic/Programmable Electronic Safety-Related Systems:

Generic Aspects, Part 1: General Requirements," British Standards

Institution, January 1992.

IFIP WG 10.4 IFIP WG 10.4, "Dependability: Basic Concepts and Terminology," IFIP Working Group on Dependable Computing and Fault Tolerance, October

1990.

Interim Defence Std 00-55 Interim Defence Standard 00-55, "The Procurement of Safety Critical Software in Defence Equipment," Parts 1 and 2, Ministry of Defence, 5 April 1991.

Interim Defence Std 00-56 Interim Defence Standard 00-56, "Hazard Analysis and Safety

Classification of the Computer and Programmable Electronic System

Elements of Defence Equipment," Ministry of Defence, 5 April 1991.

ISO 9000 ISO 9000, "International Standards for Quality Management," May 1990.

ITSEC 1.1989 ITSEC 1.1989, "Criteria for the Evaluation of Trustworthiness of

Information Technology (IT) Systems," GISA - German Information

Security Agency, 1989.

ITSEC 1.2 1991 ITSEC 1.2 1990, "Information Technology Security Evaluation Criteria (ITSEC)," Provisional Harmonised Criteria, June 28, 1990.

Management Plan

Documentation Standard

"Management Plan Documentation Standard and Data Item Descriptions

(DID)," NASA, 2/28/89.

MIL-HDBK-347 MIL-HDBK-347, "Mission-Critical Computer Resources Software

Support," Department of Defense, 22 May 90.

MIL-STD-882B MIL-STD-882B, "System Safety Program Requirements," Department of

Defense, 30 March 1984.

B-3
IDENTIFIER NUMBER AND TITLE

NCSC-TG-005 NCSC-TG-005, "Trusted Network Interpretation of the Trusted Computer

System Evaluation Criteria," National Computer Security Center, 31 July 1987.

NCSC-TG-021 NCSC-TG-021, "Trusted Database Management System Interpretation of

the Trusted Computer System Evaluation Criteria," National Computer

oecuriiy v^enicr, /\pni lyyi.

NPR-STD-6300 NPR-STD-6300, "Management of Scientific, Engineering and Plant ooiiwdTc, wiiice ui i>cw rrouuLuoii i\.eaciurs, u.o. ueparuncni oi Energy, March 1991.

i>o/\ opec. OO-IO iNoA opec. oo-io, oecuriiy vjuiuciiiics lor v„vjivion,v„ ooiiwdre

Development," National Security Agency, 10 July 1986. NSWC TR 89-33, "Software Systems Safety Design Guidelines and

Recommendations," Naval Surface Warfare Center, March 1989.

Ontario Hydro Standard "Standard for Software Engineering of Safety Critical Software," Rev. 0, Ontario Hydro, December 1990.

r iZZo ^^UK/Ar 1 ) POOS rizzo, 'YnR AP^^^ QtonHirrl fr\r QriftM/orA ^loff'tv; Plinc " Thf> Inctiliili-> r>f (^ui\/AF 1 ) oiaiiudru lor ooiiware odieiy rians, ine insiiiuie oi

Electrical and Electronics Engineers, Inc, July 19, 1991.

Product Specification

jjocumeniaiion

"Product Specification Documentation Standard and Data Item ucscripuuns z/zo/oi'.

Programmable Electronic "Programmable Electronic Systems in Safety Related Applications," Parts

1 onH 0 T-foolth inH ^ofr»l\/ T^v^^i^iitix/f* 108*7

1 diiu z, rik^dHii diiu odiciy hacwulivc, lyoi.

RTCA/DO-178A RTCA/DO-178A, "Software Considerations in Airborne Systems and

Equipment Certification," Radio Technical Commission for Aeronautics,

March, 1985.

SafelT "SafelT," Volumes 1 and 2, Interdepartmental Committee on Software

Engineering, June 1990. UL 1998 UL 1998, "The Proposed First Edition of the Standard for Safety-Related

Software," Underwater Laboratories, August 17, 1990.

B-4
V5D<H

C/2 ?^ .s a c .2

•o .S cEBo scCO S -a

T3

T3Cu8 o I I

.iS '"3 T3 C

B- « « C c

-' i 1 c o Q O scoc

•ac

'CZo

•a

a -a

'C c

•o T3 c c E E E E

0^O ^ Q CeJ E .ia O 3

•ocC

ISQ 2cE3

">o

02BU3

5"

'cc<I PQ

e2

r-00ON

CO<uaon<

C/3

oo

ON

oo

on

W 7 U

OF iin^ E ooONooo

(N

(N 00

ONo

C/0 P H

.2 :S < ^ o ;2 3 o Q 02 a ^ O Dti tu Z S oo zo

a;u00WQzU o

i3coo

C33

a*3

'c

x:oc3oo S Q aE E ^ E s §

D. - z -CO03 H) W O O - ^Q Q c 5 5 o too

53 o

C/5 eg

•S E II I- o on l_r

M-1

— o ^

I- «^ E^ i ^ ^ o

fco 3 <i2 S o ^

D- i« o O o on

T3 .S 3o o

.2 - o

<o 2 3

a- § eg _ >^ — ° C

c3 E c3 3 I >3

D.3O

fco S

.S w) c M

CO

v>

O <u is

C/3C

P3o 3 — x: S- « ^ 3 -o O ° .9 3 s 'I •§2 3 5

.s

T3

—E

<D • 

O o i2 > ^ E c O X) c

.2

•3 o & a 3 so

.E ra 3 ^— VI

•3

03 3o 5 -^^ 3 ^ Co o 'r.

c/2

C ^

00

O

O

O

O u, ^2 3 60 O .-

•oo

-5o

.2 S

'-^ ^33I 3 O O

•i3 o o

_r o

.z. o

13 3

Ml3•3oc2

ci, ooo

03 Eo

13

13

bOc

'2. 0 o 3 O 1

-5

H c/2 ^ 03 --^ 3 ^O ^

X) § 5

bO3

CO

VibO

B-5
00<Hon 3 co

•a

Oh

•g o c c I ^% % > 5 <u <u O -o S S cc co c

"C

X)

-sc

<Zh Q

gcc

T3

•oc

CO

C c .2 o c

a. <D cc Q O Q S c

'C

JOsc

<D Q

C/5 Q

on co

03o

•acS<H 1 < 2 < E P dE dO

00<aon ZouQ ooO 'CJ •a u o ^

a, s c —

on 3 S I s S o o. o .'^ .t; ^ U X

•5 3? 3 (U " ai O W 3

.^2 3 c

<u >~> o M D. •a 3 £ 3 o 3 O

u: .E O ti 3 2. o

>- .S2 > <o ° i I i o >

C3 <U 3 -S O

<*5 3

•a3 5 ^

CO

f5 > 3

00

(U .— 3 r3 3 2

"to 3

-3 3 'i

v) -3 8 I s

D. I^ ^^ W W> 3 5 I 5 ^4-1 , o E 13 - « .S2 on T3 '-^ •C _, 2 y =^ C

CO c3 > o a 2 ^

is o o•Si E .5^ S h c3 b 3 .a 3

a" .'3 cr O u- o o 3

2i « oO 3 i2

CO o o -a

CO

COo

;3

'Co

c/3

O " E «o - o "

ji: 3 o o. o C

.3

q

X

C/5

u 3Oa

T3OO oXo

C/3 ^ § E

D. I Eq o

CO3

CO3

•ab3O

Xo

"o

ts3oo a3

CO?oc13Oo

.22

-a3

CO

COo

•a

B-6
C/3 <

.JSC

C

£i .Si .Si T3 X5 -O

.| "S "S O .= .= WOO Q Q Q

T3O

T3C

a.

03

r3CO

•aO

.1

toc

'S

•oo c £

•2 E co = o ^ (1 -J S & S O

T3O

o- o ^ g.s E 13 E =

T3C

X) T3 O O

T3 -O C C O <D E E E E o o B B co

Q.

Q.

.Si ^1 ^ o oc

•o

T3 T3 O O

T3 "O C C O O E E E E o o B B

T3ocoO -O o o O "O c c o o E E E E o o

onQ

V3

00

T3

00 oO o 1=;

<N

(NO

00< . lo E—' S 00

iri

C

•c3< - S 2 "5 >^oo o O ^3 3 Si2

CSo

00

LU «

(N

00SIS 00 o

(2ti Uon c

•oco3

±3

1/5

iS T3

•aEo

-3

.5 o

*^ -S 2 , 3

<U on 3 U 3 o >O "O

-J o

•o " 3 -S

D- ^ C 5 .a 3 M . . O

>.

"~ oo ^ ^ co3oc

,3oo

on

O

O ,

00

T3O

(U ii i I ' i o o S o — -o o -S ^ o o E

•3

O £ -3 o

CI. 3 ^

T3 3

<U c/2 ^ -O C O w O

J= 3 -a

V5 0 =2 o o

CO

on

13o s M O

M) D.

it: 3 O 5^ E o

a.o £

.2" o ^ a - o ^ §1 ^ b ii

W)

O c I "o E £i X) CO E g O 3

.1 3

CT

on

C

COOC -o

u- CO o ^ oon 2C 3 ^ O t £ = " I

CO ^ 2 3o3 ^ I o o S E

on

OO o >> O O - fc — 3

CO 5O o c

•£2 M

tl3 O • — on i3 3£

soB 3 «^ E ^

CO

O

c/o CO CO _B ^ CO

O

g E

c3 CO—o ^^ 3 o o

-o 3 CO

to so c o azXu

13c

CO

=3CO T3

13

CO3

CO -a co

•a

a.3too

1.^ 2 13

so

COo3CO T3 oOO

scco 3

T3 u. o 33O

t:o 3

B-7
PH< T3

T3CE co

T3C

•o

T3 -O

T3 T3 C C e E E E o o cQ co co 2 Di cc c

in

•o

-a -o c c E E E E o o scco co ^ .2

00 £ "O

ON o C c « — o ^

i: D- .S >> 3 a. ^ C

o*

M >^ <u o — G 'C ^ .£ o ^

"S "S "g "S

<D 1)

W <U ID

QQQ

QQ c

T3EEo

on

00O<uH oOSoONO00

<N8

SIS 00

'3 o oO

<N8 6 ^ 2 <

l±l 00

oo " ON^ OC3 <

S3 J2 8 U

oo ^ ^ < ci: ly-i H < K oo ^ (N CJ rj ^ ^ H z oUooQ cE

•a

t>0

13o

i3 T3

CO c

Hi

(U o S o

133Co

••6

«5E TD £ E

toc3

a-

'co

C3 • 

2 -S 1^ C/3

si

V5

O

(U

O

t- c« >

)§ =5 « <— <- « C != -s «Eo

CTC

x: 3

a-8 =^ • O

T3O

O ^

Si = S 2 3

'coc

T3O

•a

03

T3Coc o TM

t-c (0o c o 2O ,2

tso -a o E2

too U

T3C

03 I ^ C -3 co

•a3oX

13 cC

03bO o

T3OE3

03 a

_3:a

i3 s « c E S o' u

03E 3

03

c/^ 2 E

03 ^ ^ c c 2 .2

03Eo

x:

c5E^ c

13 =

-o ,o g

13E _ & i

.2: «- o O ^ 13 2 g

13^ ^

r\ fr\

B-8
inDH<H

C/2 cw

J

T3C

in

-o c 5 S S .s .s

"S "S

_c c c

CiM < <u <u Q Q Q

-O T3

T3 -O c c E E E E o o

T3Cc c

.2 .9 c c

T3c

C3

CI ^ .Si •a X) S .S Q Q

a.c ' a « •- "Sec Sec S Q Q

.E :g .E

"S "S "S c c c

en <e o o o Q Q Q sco

C/2 Q 00

ONI o

inOo o ^ li O ^ -S ^

•3 s ^ §_ O H -E S

On <^

c5

O

Q

Oh

•5

O Cii , < < Q

cy S Z 00 ^

C/5 C/0 < 5 5 Q

<l < uCQ(Noo

00QHon Zou00Q ~c n.c

<u •- o C u

<o o

ST i3o oca. - 3 — = CO •2 -a .S M .2 —

•a '8 -s & & 2

bOcoc o O (U c

I- o

13co

2> E 3 O 3 CO T3 C T3

"cOO3

CO -a « D. ^ 3 s e

^-

M

O- O o

«^ r-S O o . o c ex O X> ^ "S C C

<D •- £ 1 3

1^

CO

c3

y o i. 3

,cO t/j - E S 15 o o ^ D. 2 >- S3 T3

CO

C 1 13

-S ^ o o zu ccoaoc3

=^ lo

—i C/2 t/2 c« 1 >^ 8 ^ Q CO o ^

"bb § D, § occo

•a

CO 21 co

COc.

T3oO

bO ^eg

B-9
H<

V2

Q S

•Io =

•5 s « t3 -O cQ coo

T3c0 CO oQ o

"•oc

o. c 3 3

0£ a:

C/2 QH <Q< o a

00 < & Q

ON^ 2 W CL, oo

T3

!3C

i3 on

<uocoQ <in

Si

U ^ W-) t-H

00

ONm<Ua00<

00

00

OnI o T

On

00

00

H zoWQ iO <U II

T3Co O bO E £i cEE

-5

£i E

<- ^ o w c o

.2 S o -c - « o <u c c

::: 8 a— o> c «

•o S

<u

—

C

•a o C 3 E

0—003 r- W2 > -5 2 y

03

Q °

03- 03 c3

T3 -o .r:: O g J3 g o _2 2 'i

?5 03 o o 1 ^

t/2 3 o 2 > >^ c 3 t«

O

O <D t;^ 1 2.f

03 bJ3

O

Obcoo3o3

55oE

lUO^ c ° E ^ b i I

<u

1-

—03 > on ^ 3o T3 O

"s § S 2

|1cr j3 •O 1^

03 •-' > 3

'5-' E .32 co 2 2

fcb «i 2 «

D.

_

.2 o >

oj 3 3

03 bO Eo c > o3

•oo

0/5 c

"U .2

a. > c0

•a

03

k. :^

-s j3

bOC s

•3

03 0

_3

03> 02

an 2

03 m, T3

03 c«

u«bo0

D.

bO 3 0

'bb D. •c0

the Ot_i 0

bO3 zu

boc

•3

bO c

03

o2 =

<D 0/5 bX3 O

0/5 "« 3

T3 T3

mo Om0

an

C

ure >.

rsio AO nc

03 T3 a.

<U 3 0/5 E

<U '0

13co•3o3O ^ E a

03O bO a 3 3 0/3

bO3

•3

03 wOU6z

B-10
on<H

T3C

a.c

ISQ

a<uc

•o

T3 "O O (D •O T3 C C s s E E o o

T3Co

S3

on _oac

•a

<ocgc

'3

C

t3Co

•oco

-S -s 'B o •§ -S

'•^ s s S Q Q

T3Cp "S o .S

T3Caacoocgco ocoQ o

•ocop o p

00

ONI

to

(N

(N

i-i

on

so

moI OHUUz o6H6O ^ <N 2 2C 00 O ^ p

_ ^ ^ D oo oo Co

Oh ^5 j ^ >9 " E '-^ ^ S S w oa o = E oC <N

c/3 C3

>> on oo S < ^

43

O 3 on

<^

u rr

H

H < oo6uonUZ

inooo

CnoocaaEc a- coE

C/3 < c - :-3 o ^ <

5j X) 2f P in

H

B (N

T3

— = O c 3 .2 I ^ ^ a, g cj i ^

72 - P '3 "o ^ w ^ 1 c O o ci: S Q H Zo

D2UonWQ o « o c " o .la c c

(D •- E E 3 «

cr o

<!-> is B -§o O occ o

O. eg s

•ti ,P

•5

C

£i E 3 o C o ^ o S E E 2 o ^

1- ca 2

03

O

C 1=1 c

<uE3 5 2 K S -o

V3 Ui

<= S O CI, D. £O

EDO C

W) 3 o ^ O u- E -S

CX

O

r- <U

13

Ui 2 o c

T3

W 2E .2 2 ^ — <^

O

=^ 3 C o o

.2 "3

a. o

7; " o >• - E

>^ 3 3 O t/5 3 c o '35 U (L) c

033o E

to '3 •a 3 C X3 P

>^b 3 0 C 0 g3 0

<u Q, K

£i JZ

bO30

a. <U >^ T3 0 03 0

•c 0 0

a. 'C 0

03 0 CO to

C/3 c 0 E E

_3 c

03 C/2 c > 3 0 0 •0

azu 0E

"o

a •0

per mo coco

D.

£3oc

'S

0- S

fcoo c

'5, o2

B-11
00DH<H

CO sc §E

.2 £ c ° I B

X)

"S "S c c c

iZ^ (Z^ o o o Q Q Q

T3C

a.

•2 = 9 a* O Dti o

T3c

T3

T3 c E

.2 £ I ^ co

T3C

D.

a. < 1 :S S i "S 3 3 C S Q

00

00

00

On Sio <

ON o in C 00 E TM < 13a. o - j ^ ^ S &, <? Z

03 3 dj U £ a

E<

Si

u c W 2

T3 .2 oON

o\<az 5 zou00Q

133i o • S o E .B"

i2 ^ § "S

C13

C/2 <*-<

-§ >O "O

03 c3oc g o o

C/2 (U ^ I i o E

•s .= 8 — E « ^ o

-B • 3E

i3c>o ?

03o „ c^3

lU o 3 2 • = O c/) ^ o ?1 : f— — _ O ^ .S E I I g J3 O £ g 3

k«o 3 = 2 E a

Oc

<u

<U o

CIg o

•o

C

—< ^

Ou->l-l E ^

.i2 53 = 3 3 03 c o .2 2 2

D. =

C3

C6Oocc£3 3 o n c £

•2 o cw

_>"o>c o ^

>. o

Z3 03 C O 2 - zu 3

bo obO c/5

eg2'3 2

bC

eg TDo P !=

13c

C3c

.ta oP

B-12
C/3 <HC/3

Co

T3CcC3

Q

O

C/2 QQ

00

00

ON &0c oONoOnO00

00 S Q 1 2 • S =

>r ^ X) 00 00 < E ^ =3 1a

00

NO

—'

NO00

ON

"2 ^ Z S 00 <

nOOO

On

(NO

00

00 S

S:i z ZouQ cC ^

t2 (u

II2

.a D.

•> ^ ^ o

"S

bO 1- c o O

-So

a,c> § 2 i «

C3

U

Q ? «

k- a. S o o s « _ •5 o

<^ CO c O O T3

i2

O 3 O N O ^ « _

03

T3O

O E S3

O i o ° 13 O o ^ l-l O D. C ^ o -o

o. s co

P3

O

O -ii o

l"i 1 §! C X

r3 o O 5 o ^ o > « c x:

_>> o ^ C t/5 O 5 3 X3

i2 « Si I I o E 5 o

•oc 5 53

a ^ 'y; O i S g " y o 2 = T3 C S Sozu

•o3

T3C

Si « § - ^ - C a I- ^ C3 Ml eg £ ^ c/2 O ^ b e E ^' a o 3 — _o ,o _u

"o '> CT" 2 "> '> '> c o S D. o o o >—I k. ^

' U( k. c

T3

— E _ T3 c w o —

•a

M ' D. > U003

.Si o > O 1- ^§ 3a>

ys«c

•a § .2 i|E .5

socC

B-13
<

.s

'"^ c

^5 S ^ S 5 i

.2 E S .2

.S S o o 2 Q S S c aoco cc CC2gc c

T3Cc oc

-o

T3 "O o o

T3 T3 c c o o E E E E o o

onQH

on

Q § ^ Q

00 /-^ CO ' ^ H < U 00 ly-i

H

rd

O <^ <N s a s:

00E o8oONo00 ooE 9 8 <Q<

00

(N

^'

00^w 2 Q O

ON LL, cE< ^ — >. oo o •- t>o o 5 U

-o .2 J2

'3 E O <

(N

CM

00

l±l 00 zoU00WQDazo E i3

'5 ~t o 3 E o 5

C/5

CO o > o o o t 2

CT 03 •- X>

•g o ^ c _ o

D.

O

•o

<4XC

C

w <^ o , T3 O

a' ? 3 ° 3 O

i3 '5b

=^ o o —

o ^

c/1 O.

C CO •2 ^ = IS

C3

a.

G.

C33 >. 2 s O 3 caO3

•aou.D,

03bOC

c5

c3 <U O 1- o E S -o

iti 2

Cl- "O '3 O « C 3

2i

C o c3 c .t; o ^ oX o

•i o 5 .S 5

133O•3

T33

C3

toCE

C3

c« _

C

C3

O

CJ 'ZZ 'S OO 3 O 3 o 2 X) « 8 b

•3

w

O 3

T3

O 2 3O T3O o

c/5 o5 o

izi o I = al o

03 03 E 2

on

O 2 ^ 1c3EQ 23o3 _ b

03 -s <D —

t/)_ 3 .3 2

bO3

•3

CJ '3 3 on 2

03 o

w) E c o

'S on R >" O 1/2 E ^

(D

O

"o. >. S S c o , .2 S 2 ^

03 t/3 E

03

u-bO

03 •53d « 55 -

B- 14
00<

Hon

t3C

•o

§ "S J «

o

o

o

X

a.

c/5 <L> _oil

•a Q

c/l

"Ho

^ J

T3G

Cu

COa

O

T3 -O c c

.2 .9 c c

co

>< •3c

(UD,

Q,C

T3O

r-

-la Q

a

o.

n.

C '3 Q S

H00

c

E

on

<U

C/3 ^: <

o <u I—I ^ ^2 = 0

n, -2

fe O Ci5 E

00o

00

00

<

Q

<

on <

Q

IJL, U00

ON

(NO

"2

ooU

W

IC/5

oc

ON

00

ON

§ u

Q 00 O <

ON

on 5

00

2

on W

7

on on

Q

<

on •<

2

O

a;u

w

Q

5 3

c

.2 « 5 3

" y

X r2 =

t CO

"5) M

-2 c w o

.S

•ao

0..2 X C/3 0)D- 3 1^

CI. _

o

q i

^

-3 E ^

T3 E

§ E a «^ > C/3 T3

r- w c/i

2 ;^ 1

> o

a ^ s-

•S _

S3 ^ 1/3 w

o -5

- ^ ^ 8^ ^ •so

o §i

c -o

C3

P3 ^ ^ (/3

J= .E

o

•a3 >^ co

E

0 .5 O

1 E

.5" 'S

(u ^ E W — o O Is P3

5 S

o q <y _ Ss

T3 ^

V c t:^ 2 S § 0*00

o c i2 floe

g 3 E

C c u

<^ c 3

o 2

o

3

T3O

E

o

0)

•5 .r-, 5

U

cg

3

•a> ^

O O

O 3 ^ X >-> (U

c/3 w

o

c

bO 03 E

'5 =^ b B

3o

bOc3

O bO

B-15
on<

00

T3C

O.a.

T3D

C

T3

T3C

1 "S i .s .s "S ^ § "S -g "S §

_ w C C C '3 Q S Q Q Q S

•oc

D.

03 T3 T3 T3 -O C C

E E

- - £ E ^ o ^ o o j5

S Q O

co

00Q

00

I

oo

53 ON

o •

= 2 S

.2 00 00 <

!±3 O Dei E

O

<

U

<N

1

oo

^ ^ E

H <

00

ON

(N

00 S

U I 00 00

Z

o

U00Q

co

o

E °

O

1-1 (U

5^ ^

03

'E

c

03>

o

= o

= i

T3 w

W C " I

E tc i> S >

fcb <= p o

D.-S

a o

-S = O 03 11o O C

1— C/2 O C c .9

n i

b p

a •o

E i

O o

a > a

-O 03

.2 bb ^=^ O —

b o

o S2 1

'c o ^ 11°

u

3

O

-Sa

B-16

ir u s. GOVERNMENT PRINTING OFFICE 1993—3 h 1 -J 3 2 ' 8 3 0 3
ANNOUNCEMENT OF NEW PUBLICATIONS ON

COMPUTER SYSTEMS TECHNOLOGY

Superintendent of Documents

Government Printing Office

Washington, DC 20402

Dear Sir:

Please add my name to the announcement Hst of new pubHcations to be issued in the series: National Institute of Standards and Technology Special Publication 500-.

Name

Company

Address

City State Zip Code

(Notification key N-503)

ly/ J. kJ A. Technical Publications

Periodical

Journal of Research of the National Institute of Standards and Technology -Reports NIST

research and development in those disciplines of the physical and engineering sciences in which

the Institute is active. These include physics, chemistry, engineering, mathematics, and computer

sciences. Papers cover a broad range of subjects, with major emphasis on measurement

methodology and the basic technology underlying standardization. Also included from time to time

are survey articles on topics closely related to the Institute's technical and scientific programs.

Issued six times a year.

Nonperiodicals

Monographs — Major contributions to the technical literature on various subjects related to the

Institute's scientific and technical activities. Handbooks — Recommended codes of engineering and industrial practice (including safety codes)

developed in cooperation with interested industries, professional organizations, and regulatory

bodies.

Special Publications — Include proceedings of conferences sponsored by NIST, NIST annual

reports, and other special publications appropriate to this grouping such as wall charts, pocket

cards, and bibliographies.

Applied Mathematics Series — Mathematical tables, manuals, and studies of special interest to

physicists, engineers, chemists, biologists, mathematicians, computer programmers, and others

engaged in scientific and technical work.

National Standard Reference Data Series — Provides quantitative data on the physical and chemical

properties of materials, compiled from the world's literature and critically evaluated. Developed

under a worldwide program coordinated by NIST under the authority of the National Standard

Data Act (Public Law 90-396). NOTE: The Journal, of Physical and Chemical Reference Data

(JPCRD) is published bimonthly for NIST by the American Chemical Society (ACS) and the

American Institute of Physics (AIP). Subscriptions, reprints, and supplements are available from

ACS, 1155 Sixteenth St., NW, Washington, DC 20056.

Building Science Series — Disseminates technical information developed at the Institute on building

materials, components, systems, and whole structures. The series presents research results, test methods, and performance criteria related to the structural and environmental functions and the

durability and safety characteristics of building elements and systems.

Technical Notes — Studies or reports which are complete in themselves but restrictive in their treatment of a subject. Analogous to monographs but not so comprehensive in scope or definitive

in treatment of the subject area. Often serve as a vehicle for final reports of work performed at NIST under the sponsorship of other government agencies.

Voluntary Product Standards — Developed under procedures published by the Department of

Commerce in Part 10, Title 15, of the Code of Federal Regulations. The standards establish

nationally recognized requirements for products, and provide all concerned interests with a basis for common understanding of the characteristics of the products. NIST administers this program

in support of the efforts of private-sector standardizing organizations.

Consumer Information Series — Practical information, based on NIST research and experience,

covering areas of interest to the consumer. Easily understandable language and illustrations provide useful background knowledge for shopping in today's technological marketplace.

Order the above NIST publications from: Superintendent of Documents, Government Printing Office,

Washington, DC 20402.

Order the following NIST publications — FIPS and NISTIRs—from the National Technical Information

Service, Springfield, VA 22161.

Federal Information Processing Standards Publications (FIPS PUB) — Publications in this series

collectively constitute the Federal Information Processing Standards Register. The Register serves

as the official source of information in the Federal Government regarding standards issued by

NIST pursuant to the Federal Property and Administrative Services Act of 1949 as amended.

Public Law 89-306 (79 Stat. 1127), and as implemented by Executive Order 11717 (38 FR 12315,

dated May 11, 1973) and Part 6 of Title 15 CFR (Code of Federal Regulations).

NIST Interagency Reports (NISTIR)— A special series of interim or final reports on work

performed by NIST for outside sponsors (both government and non-government). In general,

mitial distribution is handled by the sponsor; public distribution is by the National Technical

Information Service, Springfield, VA 22161, in paper copy or microfiche form.
ocuoH

•o

a> c e -§ ® s U i2 ^ o ^ i|

es c O § 3 p-