# Patent

## Title
Attribute value properties for test selection with cartesian product models

## Abstract

A method for modeling a test space is provided. The method comprises defining a coverage model including one or more attributes, wherein respective values for the attributes are assigned, one or more definitions of value properties for said attributes with assigned values, and one or more requirements that limit combination of attribute values that are legal for the model, wherein at least one of said requirements is defined using at least one value property.

## Background
CROSS-REFERENCE TO RELATED APPLICATION
This application is a continuation-in-part of and claims priority to U.S. patent application Ser. No. 13/285,016 filed on Oct. 30, 2011 the content of which is incorporated herein by reference in its entirety.


COPYRIGHT & TRADEMARK NOTICES
A portion of the disclosure of this document may contain material subject to copyright protection. Certain marks referenced herein may be common law or registered trademarks of the applicant, the assignee or third parties affiliated or unaffiliated with the applicant or the assignee. Use of these marks is for providing an enabling disclosure by way of example and shall not be construed to exclusively limit the scope of the disclosed subject matter to material associated with such marks.
TECHNICAL FIELD
The disclosed subject matter generally relates to testing system behavior in a computing environment.
BACKGROUND
Model based techniques may be used for modeling software systems by generating tests for verifying the behavior of a computing system on which the software is executed. A model includes a set of attributes in addition to values for the attributes and corresponding restrictions on said values or value combinations. The set of valid value combinations defines the space to be tested.
In a Cartesian-product based model, the test space is defined as all possible combinations of variable (i.e., attribute) values that are not ruled out by restrictions. The size of a Cartesian-product based model is the product of the number of values for each attribute (i.e., A1*A2* . . . *An), where An represents the number of valid values for the nth attribute. One would appreciate that the size of the model can become prohibitively large, depending on the number of attributes and the possible number of values assigned to each attribute.
Depending on the testing environment, tests may be selected from large data sets or large test suits where the set of values for one or more attributes may be too large to be accommodated by the available resources in a computing system under test. In some scenarios, there may be multiple values that are equivalent with respect to a given testing. Thus, it may be desirable to select tests based on properties associates with values, instead of the values themselves.
For example, in healthcare there are thousands of diagnosis codes. Testing all of them, not to mention combinations of all of them, with procedure codes, for example, may consume a vast volume of resources. It would be desirable to reduce the number of tests by requiring coverage of groups of codes (e.g., blood vs. bone related diagnosis). Such groups may be semantic or procedural. That is, a group of values may share the same processing path and hence only one value from each group may need to be covered.
Currently, implementing a grouping such as that noted above requires writing a program or a script that transforms the original input values to the smaller set of groups or group representatives. The coding process is an expensive, time consuming step and requires skills that are frequently not available to the novice members of test teams. A less expensive and more flexible alternative is desirable that can be used to simplify the modeling of software system.
SUMMARY
For purposes of summarizing, certain aspects, advantages, and novel features have been described herein. It is to be understood that not all such advantages may be achieved in accordance with any one particular embodiment. Thus, the disclosed subject matter may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages without achieving all advantages as may be taught or suggested herein.
In accordance with one embodiment, a method for modeling a test space is provided. The method comprises defining a coverage model including one or more attributes, wherein respective values for the attributes are assigned, one or more definitions of value properties for said attributes with assigned values, and one or more requirements that limit combination of attribute values that are legal for the model, wherein at least one of said requirements is defined using at least one value property.
In accordance with one or more embodiments, a system comprising one or more logic units is provided. The one or more logic units are configured to perform the functions and operations associated with the above-disclosed methods. In yet another embodiment, a computer program product comprising a computer readable storage medium having a computer readable program is provided. The computer readable program when executed on a computer causes the computer to perform the functions and operations associated with the above-disclosed methods.
One or more of the above-disclosed embodiments in addition to certain alternatives are provided in further detail below with reference to the attached figures. The disclosed subject matter is not, however, limited to any particular embodiment disclosed.



BRIEF DESCRIPTION OF THE DRAWINGS
The disclosed embodiments may be better understood by referring to the figures in the attached drawings, as provided below.
FIG. 1A illustrates an exemplary set of restrictions defined for attribute combinations in a model constructed to test a system.
FIGS. 1B through 1E are exemplary illustrations of a model wherein attributes are associated with one or more properties, in accordance with one embodiment.
FIG. 2 is a flow diagram of an exemplary method for modeling a test space for a system, by assigning properties to one or more attributes, in accordance with one embodiment.
FIGS. 3A and 3B are block diagrams of hardware and software environments in which the disclosed systems and methods may operate, in accordance with one or more embodiments.



Features, elements, and aspects that are referenced by the same numerals in different figures represent the same, equivalent, or similar features, elements, or aspects, in accordance with one or more embodiments.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practiced without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.
In accordance with one or more embodiments, a coverage model is used to test a target system. The model defines variables (i.e., attributes), possible values for the attributes, and conditions or requirements to indicate when values for one or more attributes or value combinations for a plurality of attributes are valid or invalid. The set of valid value combinations and requirements for combinations of attributes that are to be included in the test plan define the coverage model.
The size of the test space for the model may be defined by the product (e.g., the Cartesian product) of attribute values taking into account the dictated conditions, requirements or restrictions. In a Cartesian product based model, the size of the test space is approximately proportional to the product of the number of values that can be assigned to each attribute. In one embodiment, to reduce the number of tests for testing the system, a subset of the attributes whose combinations that are to be covered may be defined by way of applying an algorithm (e.g., a combinatorial algorithm) to filter out certain attribute combinations.
As such, in one embodiment, a combinatorial algorithm is used to generate a relatively small set of tests that satisfy certain interaction requirements between selected attributes. The requirements for the combination of attribute values may be provided as input to a combinatorial test design (CTD) engine. Given a Cartesian-product model with n attributes, the combinatorial algorithm finds a subset of valid attribute value combinations in the test space that covers possible combinations of every m attributes, wherein m defines a certain interaction level.
The interaction level, depending on implementation, refers to the coverage of the selected subset of the test space, wherein the test space covers the possible combinations of m number of attributes in the set defined by the respective coverage model—m is less than or equal to the total number of attributes n in the model. As an example, interaction level two means that for every two attributes all or some valid value combinations appear in the selected subset of the test space. Empirical evidence shows that most bugs may be found when testing the interesting interactions among a relatively small number of attributes.
In accordance with one embodiment, Cartesian product based modeling may be implement to allow a user (e.g., a user not skilled in developing test models by way of scripting) to define relatively sophisticated coverage requirements without having to write scripts that transform input data. A semantic abstraction of the model may be thus inherently induced by providing an interface that allows the user to associate properties with attribute values, and enhancing coverage requirement syntax to allow conditions based on these properties.
In one implementation, the user may interact with the provided user interface to (1) generate test suits by defining requirements based on properties (as opposed to restrictions on properties) and (2) select a test by defining properties and requirements for the properties associated with attribute values. For example, a system may be modeled based on the following three attributes: (1) hardware platform, (2) operating system (OS), and (3) host bus adaptor (HBA). This hypothetical system may include six different platforms, 17 different operating systems and 11 HBAs with incompatible components.
Referring to FIG. 1A, for example, in practice not all OSs may run on all platforms due to a type mismatch (e.g., Windows OS cannot run on pSeries computers), and not all HBAs may be used with every OS. Accordingly, to properly construct the model for this exemplary system, one may have to resort to using programming language expressions or alternatively define a requirement to avoid an illegal combination (e.g., “not allowed xServer and AIX.7”, “not allowed xServer and AIX.6”, “not allowed xBlade and AIX.7”, etc.).
Due to the number of restrictions involved, defining every single restriction based on the above approach may be a time consuming and error prone process. As such, in one embodiment, the model is implemented so that the system attributes may be grouped according to their common properties or type. Referring to FIG. 1B, for example, a model 110 is illustrated in which the possible attribute values are shown as a list associated with each attribute. As shown, the attribute “platform” may be assigned values xServer, xBlade, pServer, pBlade, etc.; and the attribute “OS” may be assigned values RHEL5.x, Win2k3, AIX5.x, AIX6.x, etc., for example.
Depending on implementation, the matching between attribute values may be based on the attribute values association with a certain property. For example, certain OSs may run on both types of platforms, but others may only run on one type of platform. Accordingly, referring to FIG. 1C, each platform may be associated with a type “x” or “p”, and each OS may be associated with one or both types “x” or “p”. In other words, the properties for the attributes are defined per attribute, and the respective values for the properties are of a certain type.
As such, referring to FIGS. 1B and 1C, attributes values may be defined as a list associated with the attribute, with property columns, where the properties are given per attribute and their value may be of any type. This is equivalent to defining a function over the values of an attribute. Partial functions (that are defined on part of the value domain) may be also of use. In one embodiment, for values for which the function is not defined, the default is to leave the current value.
Referring to FIG. 1D, attribute properties may also be used in visual or graphical user interfaces to present or mark in abstract the limitations or restrictions associated with one or more attributes included in the model based on the semantic information given in the property values for a attribute. For example, depending on implementation, a certain color, font, or other property associated with a character that is used to represent a attribute may indicate that one or more combinations are required or illegal. In FIG. 1D, for example, the illegal combinations are shown with italic and bold properties with the attributes abstracted.
As such, adding properties to values may be done using an interactive graphical user interface (GUI), as part of modeling a test domain by way of a mechanism that imports tables in various formats or based on an analysis of the frequency of occurrence of values. For example, in an example scenario, the most frequently used 10 values or 10 representatives of the next 90 most frequency used values may be considered. Of special interest are properties that describe methods to mitigate the transformation from ICD-9 to ICD-10 diagnosis codes in healthcare systems.
Referring to FIG. 2, static analysis of the code under test may be optionally used to produce groupings based on values that are equivalent with respect to the code under test (S210). In one implementation, multiple properties may be assigned to a one or more attribute values (S220). Optionally, the different methods of assigning properties to different attribute values may be combined. Test coverage requirements, on attribute value combinations, may be defined by allowing the use of property names instead of attribute names (S230).
Once the test coverage requirements are defined, a subset of a given test suit may be selected, wherein the subset satisfies the requirements defined by the test coverage (S240). Accordingly, properties may be use in place of attributes to define a coverage requirement. For example, existing tests (or part of them) may be represented in terms of different properties, if the original values are missing but the properties may still be deduced. The respective information may be used during test selection (or coverage analysis) when there are coverage requirements that relate to properties.
Referring to FIG. 1E, in an example model, diagnosis codes (e.g., a set of 11 diagnosis codes) and procedure codes (e.g., a set of 11 procedure codes) are used to define attribute values. In this example, frequency (e.g., set of {Low, Medium, High}) and importance (e.g., set of {Low, Medium, High}) are the respective properties defined for the diagnosis code and the procedure code. In the above example, the following combinations may be considered for the purpose of testing the model under test:

    
    
        1. All combinations of Diagnosis Code and Procedure Code (11*11=121 combinations)
        2. All combinations of Diagnosis Code Frequency and Procedure Code Importance (3*3=9 combinations)
        3. All combinations of Diagnosis Code Frequency and Procedure Code (3*11=33 combinations)
    
    


Requirements defined in #1 reflect known CTD practices involving the designation of combinations of attributes to be tested together. Requirements defined in #2 and #3 are different in that instead of designating combinations of attributes, combinations of attribute properties are designated for the purpose of testing. This approach may be applied not only to CTD test generation but also to test selection, depending on implementation.
It is noteworthy that in the above example, some of the combinations included above can be ruled out by restrictions. For example consider:

    
    
        1. Don't allow Diagnosis Code=31415 && Procedure Code=A14
        2. Don't allow Diagnosis Code Frequency=Low && Procedure Code Importance=Low
        3. Don't allow Diagnosis Code=31415 && Procedure Code Importance=Medium
    
    


The above disclosed method enhances Cartesian product based modeling in a way that allows a user to more easily define fairly sophisticated restrictions without having to use complex programming language syntax. Furthermore, a semantic abstraction of the model is induced by providing an interface that allows the user to associate properties with attribute values, and enhancing requirement syntax to allow conditions based on said properties. Based on the above, one or more embodiments may be utilized to make the modeling of a system simpler without reducing the efficacy of the model, resulting in reduced costs and increased quality.
In different embodiments, the claimed subject matter may be implemented as a combination of both hardware and software elements, or alternatively either entirely in the form of hardware or entirely in the form of software. Further, computing systems and program software disclosed herein may comprise a controlled computing environment that may be presented in terms of hardware components or logic code executed to perform methods and processes that achieve the results contemplated herein. Said methods and processes, when performed by a general purpose computing system or machine, convert the general purpose machine to a specific purpose machine.
Referring to FIGS. 3A and 3B, a computing system environment in accordance with an exemplary embodiment may be composed of a hardware environment 1110 and a software environment 1120. The hardware environment 1110 may comprise logic units, circuits or other machinery and equipments that provide an execution environment for the components of software environment 1120. In turn, the software environment 1120 may provide the execution instructions, including the underlying operational settings and configurations, for the various components of hardware environment 1110.
Referring to FIG. 3A, the application software and logic code disclosed herein may be implemented in the form of computer readable code executed over one or more computing systems represented by the exemplary hardware environment 1110. As illustrated, hardware environment 110 may comprise a processor 1101 coupled to one or more storage elements by way of a system bus 1100. The storage elements, for example, may comprise local memory 1102, storage media 1106, cache memory 1104 or other computer-usable or computer readable media. Within the context of this disclosure, a computer usable or computer readable storage medium may include any recordable article that may be utilized to contain, store, communicate, propagate or transport program code.
A computer readable storage medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor medium, system, apparatus or device. The computer readable storage medium may also be implemented in a propagation medium, without limitation, to the extent that such implementation is deemed statutory subject matter. Examples of a computer readable storage medium may include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk, an optical disk, or a carrier wave, where appropriate. Current examples of optical disks include compact disk, read only memory (CD-ROM), compact disk read/write (CD-R/W), digital video disk (DVD), high definition video disk (HD-DVD) or Blue-rayTM disk.
In one embodiment, processor 1101 loads executable code from storage media 1106 to local memory 1102. Cache memory 1104 optimizes processing time by providing temporary storage that helps reduce the number of times code is loaded for execution. One or more user interface devices 1105 (e.g., keyboard, pointing device, etc.) and a display screen 1107 may be coupled to the other elements in the hardware environment 1110 either directly or through an intervening I/O controller 1103, for example. A communication interface unit 1108, such as a network adapter, may be provided to enable the hardware environment 1110 to communicate with local or remotely located computing systems, printers and storage devices via intervening private or public networks (e.g., the Internet). Wired or wireless modems and Ethernet cards are a few of the exemplary types of network adapters.
It is noteworthy that hardware environment 1110, in certain implementations, may not include some or all the above components, or may comprise additional components to provide supplemental functionality or utility. Depending on the contemplated use and configuration, hardware environment 1110 may be a desktop or a laptop computer, or other computing device optionally embodied in an embedded system such as a set-top box, a personal digital assistant (PDA), a personal media player, a mobile communication unit (e.g., a wireless phone), or other similar hardware platforms that have information processing or data storage capabilities.
In some embodiments, communication interface 1108 acts as a data communication port to provide means of communication with one or more computing systems by sending and receiving digital, electrical, electromagnetic or optical signals that carry analog or digital data streams representing various types of information, including program code. The communication may be established by way of a local or a remote network, or alternatively by way of transmission over the air or other medium, including without limitation propagation over a carrier wave.
As provided here, the disclosed software elements that are executed on the illustrated hardware elements are defined according to logical or functional relationships that are exemplary in nature. It should be noted, however, that the respective methods that are implemented by way of said exemplary software elements may be also encoded in said hardware elements by way of configured and programmed processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) and digital signal processors (DSPs), for example.
Referring to FIG. 3B, software environment 1120 may be generally divided into two classes comprising system software 1121 and application software 1122 as executed on one or more hardware environments 1110. In one embodiment, the methods and processes disclosed here may be implemented as system software 1121, application software 1122, or a combination thereof. System software 1121 may comprise control programs, such as an operating system (OS) or an information management system, that instruct one or more processors 1101 (e.g., microcontrollers) in the hardware environment 1110 on how to function and process information. Application software 1122 may comprise but is not limited to program code, data structures, firmware, resident software, microcode or any other form of information or routine that may be read, analyzed or executed by a processor 1101.
In other words, application software 1122 may be implemented as program code embedded in a computer program product in form of a computer-usable or computer readable storage medium that provides program code for use by, or in connection with, a computer or any instruction execution system. Moreover, application software 1122 may comprise one or more computer programs that are executed on top of system software 1121 after being loaded from storage media 1106 into local memory 1102. In a client-server architecture, application software 1122 may comprise client software and server software. For example, in one embodiment, client software may be executed on a client computing system that is distinct and separable from a server computing system on which server software is executed.
Software environment 1120 may also comprise browser software 1126 for accessing data available over local or remote computing networks. Further, software environment 1120 may comprise a user interface 1124 (e.g., a graphical user interface (GUI)) for receiving user commands and data. It is worthy to repeat that the hardware and software architectures and environments described above are for purposes of example. As such, one or more embodiments may be implemented over any type of system architecture, functional or logical platform or processing environment.
It should also be understood that the logic code, programs, modules, processes, methods and the order in which the respective processes of each method are performed are purely exemplary. Depending on implementation, the processes or any underlying sub-processes and methods may be performed in any order or concurrently, unless indicated otherwise in the present disclosure. Further, unless stated otherwise with specificity, the definition of logic code within the context of this disclosure is not related or limited to any particular programming language, and may comprise one or more modules that may be executed on one or more processors in distributed, non-distributed, single or multiprocessing environments.
As will be appreciated by one skilled in the art, a software embodiment may include firmware, resident software, micro-code, etc. Certain components including software or hardware or combining software and hardware aspects may generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, the subject matter disclosed may be implemented as a computer program product embodied in one or more computer readable storage medium(s) having computer readable program code embodied thereon. Any combination of one or more computer readable storage medium(s) may be utilized. The computer readable storage medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out the disclosed operations may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages.
The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
Certain embodiments are disclosed with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer readable storage medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The claimed subject matter has been provided here with reference to one or more features or embodiments. Those skilled in the art will recognize and appreciate that, despite of the detailed nature of the exemplary embodiments provided here, changes and modifications may be applied to said embodiments without limiting or departing from the generally intended scope. These and various other adaptations and combinations of the embodiments provided here are within the scope of the disclosed subject matter as defined by the claims and their full set of equivalents.

## Claims

1. A method executable on one or more processors for testing software using a reduced suite of test data, the method comprising:
receiving test data, wherein each item of test data includes one or more variables, each variable is assigned a value, and the set of all items of test data includes all combinations of variables and values of a test space to be tested;
assigning to each variable value one or more value properties;
receiving a test data requirement that identifies a value property of a first variable in combination with a second variable; and
generating a reduced test suite of data by:
identifying one value of the first variable to which is assigned the received value property; and
selecting from the received test data all test items having a value of the first variable equal to the one value, and having a value of the second variable equal to any value; and

testing the software using the reduced test suite of data.

2. The method of claim 1 wherein one or more requirements are identified based on a common property shared among at least two variables.

3. The method of claim 1 wherein identifying requirements applicable to variable value combinations based on the value property is accomplished by way of interacting with a graphical user interface.

4. The method of claim 3 wherein a color coding scheme is used to identify value property combinations that do not meet identified requirements by marking a property associated with a variable in a certain color.

5. The method of claim 3 wherein a scheme is used to identify value property combinations that do not meet identified requirements by configuring a property associated with a variable in a certain font.

6. The method of claim 3 wherein a scheme is used to identify value property combinations that do not meet identified requirements by configuring a property associated with a variable in a visually distinctive manner.

7. The method of claim 3 wherein the attribute value properties for selected variables are displayed in a table and the requirements are identified by marking rows or columns in the table.

8. The method of claim 3 wherein value property combinations for selected properties are displayed in a table, and the requirements are identified by marking rows or columns in the table.

9. The method of claim 1 wherein a value property for a variable defines a type associated with the variable.

10. The method of claim 1 further comprising testing using a set of valid variable value combinations.

11. A computer system comprising one or more processors for executing a method for testing software using a reduced suite of test data, the method comprising:
receiving test data, wherein each item of test data includes one or more variables, each variable is assigned a value, and the set of all items of test data includes all combinations of variables and values of a test space to be tested;
assigning to each variable value one or more value properties;
receiving a test data requirement that identifies a value property of a first variable in combination with a second variable; and
generating a reduced test suite of data by:
identifying one value of the first variable to which is assigned the received value property; and
selecting from the received test data all test items having a value of the first variable equal to the one value, and having a value of the second variable equal to any value; and

testing the software using the reduced test suite of data.

12. The system of claim 11 wherein one or more requirements are identified based on a common property shared among at least two variables.

13. The method of claim 11 wherein identifying requirements applicable to variable value combinations based on the value property is accomplished by way of interacting with a graphical user interface.

14. The method of claim 13 wherein a color coding scheme is used to identify value property combinations that do not meet identified requirements by marking a property associated with a variable in a certain color.

15. The method of claim 13 wherein a scheme is used to identify value property combinations that do not meet identified requirements by configuring a property associated with a variable in a certain font.

16. A computer program product comprising program code embedded in a non-transitory data storage medium, wherein execution of the program code on a computer causes the computer to:
receive test data, wherein each item of test data includes one or more variables, each variable is assigned a value, and the set of all items of test data includes all combinations of variables and values of a test space to be tested;
assign to each variable value one or more value properties;
receive a test data requirement that identifies value property of a first variable in combination with a second variable; and
generate a reduced test suite of data by:
identify one value of the first variable to which is assigned the received value property; and
select from the received test data all test items having a value of the first variable equal to the one value, and having a value of the second variable equal to any value; and

test the software using the reduced test suite of data.

17. The computer program product of claim 16 wherein one or more requirements are identified based on a common property shared among at least two variables.

18. The computer program product of claim 16 wherein identifying requirements applicable to variable value combinations based on the value property is accomplished by way of interacting with a graphical user interface.

19. The computer program product of claim 18 wherein a color coding scheme is used to identify value property combinations that do not meet identified requirements by marking a property associated with a variable in a certain color.

20. The computer program product of claim 18 wherein a scheme is used to identify value property combinations that do not meet identified requirements by configuring a property associated with a variable in a certain font.