Procedures for Performing
Systematic Reviews
Barbara Kitchenhame-mail: barbara@cs.keele.ac.uk
Joint Technical Report
Software Engineering Group
Department of Computer Science
Keele University
Keele, Staffs
ST5 5BG, UK
Keele University Technical Report TR/SE-0401
ISSN:1353-7776
andEmpirical Software Engineering
National ICT Australia Ltd.
Bay 15 Locomotive Workshop
Australian Technology Park
Garden Street, Eversleigh
NSW 1430, Australia
NICTA Technical Report 0400011T.1
July, 2004

© Kitchenham, 2004

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Document Control Section

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Contents

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Document Control Section......................................................................................i
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Contents ..........................................................................................................i
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Document Version Control .......................................................................... iii
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Executive Summary ......................................................................................iv
1. Introduction............................................................................................................1
2. Systematic Reviews ...............................................................................................1
2.1
Reasons for Performing Systematic Reviews ................................................1
2.2
The Importance of Systematic Reviews ........................................................2
2.3
Advantages and disadvantages ......................................................................2
2.4
Feature of Systematic Reviews......................................................................2
3. The Review Process...............................................................................................3
4. Planning .................................................................................................................3
4.1
The need for a systematic review...................................................................3
4.2
Development of a Review Protocol ...............................................................4
4.2.1
The Research Question ..........................................................................5
4.2.1.1 Question Types ..................................................................................5
4.2.1.2 Question Structure .............................................................................6
4.2.1.2.1 Population ....................................................................................6
4.2.1.2.2 Intervention ..................................................................................6
4.2.1.2.3 Outcomes .....................................................................................6
4.2.1.2.4 Experimental designs ...................................................................7
4.2.2
Protocol Review.....................................................................................7
5. Conducting the review ...........................................................................................7
5.1
Identification of Research ..............................................................................7
5.1.1
Generating a search strategy ..................................................................7
5.1.2
Publication Bias .....................................................................................8
5.1.3
Bibliography Management and Document Retrieval ............................95.1.4
Documenting the Search ........................................................................9
5.2
Study Selection ..............................................................................................95.2.1
Study selection criteria...........................................................................95.2.2
Study selection process ........................................................................10
5.2.3
Reliability of inclusion decisions.........................................................10
5.3
Study Quality Assessment ...........................................................................10
5.3.1
Quality Thresholds...............................................................................11
5.3.2
Development of Quality Instruments...................................................15
5.3.3
Using the Quality Instrument...............................................................16
5.3.4
Limitations of Quality Assessment......................................................16
5.4
Data Extraction ............................................................................................17
5.4.1
Design of Data Extraction Forms ........................................................17
5.4.2
Contents of Data Collection Forms......................................................17
5.4.3
Data extraction procedures ..................................................................17
5.4.4
Multiple publications of the same data ................................................18
5.4.5
Unpublished data, missing data and data requiring manipulation .......18
5.5
Data Synthesis..............................................................................................18i

5.5.1
Descriptive synthesis ...........................................................................19
5.5.2
Quantitative Synthesis .........................................................................19
5.5.3
Presentation of Quantitative Results....................................................20
5.5.4
Sensitivity analysis...............................................................................21
5.5.5
Publication bias....................................................................................21
6. Reporting the review............................................................................................22
6.1
Structure for systematic review ...................................................................22
6.2
Peer Review .................................................................................................22
7. Final remarks .......................................................................................................25
8. References............................................................................................................25
Appendix 1 Steps in a systematic review ................................................................27

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Document Version Control

Document
status
Draft
Published

VersionNumber
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1.0

Date

Changes from previous version

1 April 2004
29 June 2004

None
Correction of typos
Additional discussion ofproblems of assessing evidence
Section 7 “Final Remarks”
added.

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0.3

Executive Summary

The objective of this report is to propose a guideline for systematic reviews
appropriate for software engineering researchers, including PhD students. A
systematic review is a means of evaluating and interpreting all available research
relevant to a particular research question, topic area, or phenomenon of interest.
Systematic reviews aim to present a fair evaluation of a research topic by using atrustworthy, rigorous, and auditable methodology.
The guideline presented in this report was derived from three existing guidelines usedby medical researchers. The guideline has been adapted to reflect the specificproblems of software engineering research.
The guideline covers three phases of a systematic review: planning the review,
conducting the review and reporting the review. It is at a relatively high level. It does
not consider the impact of question type on the review procedures, nor does it specify
in detail mechanisms needed to undertake meta-analysis.

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1.

Introduction

This document presents a general guideline for undertaking systematic reviews. The
goal of this document is to introduce the concept of rigorous reviews of current
empirical evidence to the software engineering community. It is aimed at software
engineering researchers including PhD students. It does not cover details of metaanalysis (a statistical procedure for synthesising quantitative results from different
studies), nor does it discuss the implications that different types of systematic review
questions have on systematic review procedures.
The document is based on a review of three existing guidelines for systematic
reviews:
1.
The Cochrane Reviewer’s Handbook [4].
2.
Guidelines prepared by the Australian National Health and Medical ResearchCouncil [1] and [2].
3.
CRD Guidelines for those carrying out or commissioning reviews [12].
In particular the structure of this document owes much to the CRD Guidelines.
All these guidelines are intended to aid medical researchers. This document attempts
to adapt the medical guidelines to the needs of software engineering researchers. It
discusses a number of issues where software engineering research differs frommedical research. In particular, software engineering research has relatively little
empirical research compared with the large quantities of research available on
medical issues, and research methods used by software engineers are not as rigorousas those used by medical researchers.
The structure of the report is as follows:1.
Section 2 provides an introduction to systematic reviews as a significant
research method.
2.
Section 3 specifies the stages in a systematic review.
3.
Section 4 discusses the planning stages of a systematic review
4.
Section 5 discusses the stages involved in conducting a systematic review
5.
Section 6 discusses reporting a systematic review.

2.

Systematic Reviews

A systematic literature review is a means of identifying, evaluating and interpreting
all available research relevant to a particular research question, or topic area, or
phenomenon of interest. Individual studies contributing to a systematic review arecalled primary studies; a systematic review is a form a secondary study.
2.1

Reasons for Performing Systematic Reviews

There are many reasons for undertaking a systematic review. The most common
reasons are:
• To summarise the existing evidence concerning a treatment or technology e.g. tosummarise the empirical evidence of the benefits and limitations of a specificagile method.

1

•
•

To identify any gaps in current research in order to suggest areas for further
investigation.
To provide a framework/background in order to appropriately position new
research activities.

However, systematic reviews can also be undertaken to examine the extent to whichempirical evidence supports/contradicts theoretical hypotheses, or even to assist thegeneration of new hypotheses (see for example [10]).
2.2

The Importance of Systematic Reviews

Most research starts with a literature review of some sort. However, unless a literature
review is thorough and fair, it is of little scientific value. This is the main rationale forundertaking systematic reviews. A systematic review synthesises existing work inmanner that is fair and seen to be fair. For example, systematic reviews must be
undertaken in accordance with a predefined search strategy. The search strategy mustallow the completeness of the search to be assessed. In particular, researchers
performing a systematic review must make every effort to identify and report researchthat does not support their preferred research hypothesis as well as identifying andreporting research that supports it.
2.3

Advantages and disadvantages

Systematic reviews require considerably more effort than traditional reviews. Theirmajor advantage is that they provide information about the effects of somephenomenon across a wide range of settings and empirical methods. If studies giveconsistent results, systematic reviews provide evidence that the phenomenon is robust
and transferable. If the studies give inconsistent results, sources of variation can bestudied.
A second advantage, in the case of quantitative studies, is that it is possible tocombine data using meta-analytic techniques. This increases the likelihood of
detecting real effects that individual smaller studies are unable to detect. However,increased power can also be a disadvantage, since it is possible to detect small biases
as well as true effects.
2.4

Feature of Systematic Reviews

Some of the features that differentiate a systematic review from a conventional
literature review are:• Systematic reviews start by defining a review protocol that specifies the researchquestion being addressed and the methods that will be used to perform the review.
• Systematic reviews are based on a defined search strategy that aims to detect asmuch of the relevant literature as possible.
• Systematic reviews document their search strategy so that readers can access its
rigour and completeness.
• Systematic reviews require explicit inclusion and exclusion criteria to assess eachpotential primary study.
• Systematic reviews specify the information to be obtained from each primarystudy including quality criteria by which to evaluate each primary study.
• A systematic review is a prerequisite for quantitative meta-analysis

2

3.

The Review Process

A systematic review involves several discrete activities. Existing guidelines for
systematic reviews have different suggestions about the number and order of activities(see Appendix 1). This documents summarises the stages in a systematic review into
three main phases: Planning the Review, Conducting the Review, Reporting the
Review.
The stages associated with planning the review are:1. Identification of the need for a review
2. Development of a review protocol.
The stages associated with conducting the review are:1. Identification of research
2. Selection of primary studies
3. Study quality assessment
4. Data extraction & monitoring
5. Data synthesis.
Reporting the review is a single stage phase.
Each phase is discussed in detail in the following sections. Other activities identified
in the guidelines discussed in Appendix 1 are outside the scope of this document.
The stages listed above may appear to be sequential, but it is important to recognisethat many of the stages involve iteration. In particular, many activities are initiatedduring the protocol development stage, and refined when the review proper takes
place. For example:• The selection of primary studies is governed by inclusion and exclusion criteria.
These criteria are initially specified when the protocol is defined but may be
refined after quality criteria are defined.
• Data extraction forms initially prepared during construction of the protocol willbe amended when quality criteria are agreed.
• Data synthesis methods defined in the protocol may be amended once data has
been collected.
The systematic reviews road map prepared by the Systematic Reviews Group at
Berkley demonstrates the iterative nature of the systematic review process veryclearly [15].

4.

Planning

4.1

The need for a systematic review

The need for a systematic review arises from the requirement of researchers tosummarise all existing information about some phenomenon in a thorough andunbiased manner. This may be in order to draw more general conclusion about somephenomenon than is possible from individual studies, or as a prelude to further
research activities.

3

Prior to undertaking a systematic review, researchers should ensure that a systematic
review is necessary. In particular, researchers should identify and review any existing
systematic reviews of the phenomenon of interest against appropriate evaluation
criteria. CRC [12] suggests the following checklist:• What are the review’s objectives?
• What sources were searched to identify primary studies? Were there any
restrictions?
• What were the inclusion/exclusion criteria and how were they applied?
• What criteria were used to assess the quality of primary studies and how were
they applied?
• How were the data extracted from the primary studies?
• How were the data synthesised? How were differences between studies
investigated? How were the data combined? Was it reasonable to combine thestudies? Do the conclusions flow from the evidence?
From a more general viewpoint, Greenlaugh [9] suggests the following questions:• Can you find an important clinical question, which the review addressed?
(Clearly, in software engineering, this should be adapted to refer to an important
software engineering question.)• Was a thorough search done of the appropriate databases and were otherpotentially important sources explored?
• Was methodological quality assessed and the trials weighted accordingly?
• How sensitive are the results to the way that the review has been done?
• Have numerical results been interpreted with common sense and due regard to thebroader aspects of the problem?
4.2

Development of a Review Protocol

A review protocol specifies the methods that will be used to undertake a specificsystematic review. A pre-defined protocol is necessary to reduce the possibility
researcher bias. For example, without a protocol, it is possible that the selection ofindividual studies or the analysis may be driven by researcher expectations. In
medicine, review protocols are usually submitted to peer review.
The components of a protocol include all the elements of the review plus some
additional planning information:
•
•
•

•

Background. The rationale for the survey.
The research questions that the review is intended answer.
The strategy that will be used to search for primary studies including search terms
and resources to be searched, resources include databases, specific journals, andconference proceedings. An initial scoping study can help determine anappropriate strategy.
Study selection criteria and procedures. Study selection criteria determine criteria
for including in, or excluding a study from, the systematic review. It is usually
helpful to pilot the selection criteria on a subset of primary studies. The protocol
should describe how the criteria will be applied e.g. how many assessors will
evaluate each prospective primary study, and how disagreements among assessors
will be resolved.
4

•
•

•
•

Study quality assessment checklists and procedures. The researchers should
develop quality checklists to assess the individual studies. The purpose of thequality assessment will guide the development of checklists.
Data extraction strategy. This should define how the information required fromeach primary study would be obtained. If the data require manipulation orassumptions and inferences to be made, the protocol should specify anappropriate validation process.
Synthesis of the extracted data. This should define the synthesis strategy. Thisshould clarify whether or not a formal meta-analysis is intended and if so what
techniques will be used.
Project timetable. This should define the review plan.

4.2.1 The Research Question
4.2.1.1 Question Types
The most important activity during protocol is to formulate the research question. The
Australian NHMR Guidelines [1] identify six types of health care questions that canbe addressed by systematic reviews:
1. Assessing the effect of intervention.
2. Assessing the frequency or rate of a condition or disease.
3. Determining the performance of a diagnostic test.
4. Identifying aetiology and risk factors.
5. Identifying whether a condition can be predicted.
6. Assessing the economic value of an intervention or procedure.
In software engineering, it is not clear what the equivalent of a diagnostic test wouldbe, but the other questions can be adapted to software engineering issues as follows:• Assessing the effect of a software engineering technology.
• Assessing the frequency or rate of a project development factor such as the
adoption of a technology, or the frequency or rate of project success or failure.
• Identifying cost and risk factors associated with a technology.
• Identifying the impact of technologies on reliability, performance and costmodels.
• Cost benefit analysis of software technologies.
Medical guidelines often provide different guidelines and procedures for different
types of question. This document does not go to this level of detail.
The critical issue in any systematic review is to ask the right question. In this context,
the right question is usually one that:• Is meaningful and important to practitioners as well as researchers. For example,
researchers might be interested in whether a specific analysis technique leads to asignificantly more accurate estimate of remaining defects after design inspections.
However, a practitioner might want to know whether adopting a specific analysis
technique to predict remaining defects is more effective than expert opinion at
identifying design documents that require re-inspection.
• Will lead either to changes in current software engineering practice or toincreased confidence in the value of current practice. For example, researchers5•and practitioners would like to know under what conditions a project can safely
adopt agile technologies and under what conditions it should not.
Identify discrepancies between commonly held beliefs and reality.

Nonetheless, there are systematic reviews that ask questions that are primarily ofinterest to researchers. Such reviews ask questions that identify and/or scope future
research activities. For example, a systematic review in a PhD thesis should identify
the existing basis for the research student’s work and make it clear where theproposed research fits into the current body of knowledge.
4.2.1.2 Question Structure
Medical guidelines recommend considering a question from three viewpoints:• The population, i.e. the people affected by the intervention.
• The interventions usually a comparison between two or more alternative
treatments.
• The outcomes, i.e. the clinical and economic factors that will be used to compare
the interventions.
In addition, study designs appropriate to answering the review questions may be
identified.
4.2.1.2.1
Population
In software engineering experiments, the populations might be any of the following:• A specific software engineering role e.g. testers, managers.
• A type of software engineer, e.g. a novice or experienced engineer.
• An application area e.g. IT systems, command and control systems.
A question may refer to very specific population groups e.g. novice testers, or
experienced software architects working on IT systems. In medicine the populations
are defined in order to reduce the number of prospective primary studies. In software
engineering far less primary studies are undertaken, thus, we may need to avoid any
restriction on the population until we come to consider the practical implications of
the systematic review.
4.2.1.2.2
Intervention
Interventions will be software technologies that address specific issues, for example,
technologies to perform specific tasks such as requirements specification, system
testing, or software cost estimation.
4.2.1.2.3
Outcomes
Outcomes should relate to factors of importance to practitioners such as improved
reliability, reduced production costs, and reduced time to market. All relevant
outcomes should be specified. For example, in some cases we require interventions
that improve some aspect of software production without affecting another e.g.
improved reliability with no increase in cost.
A particular problem for software engineering experiments is the use of surrogate
measures for example, defects found during system testing as a surrogate for quality,6

or coupling measures for design quality. Studies that use surrogate measures may be
misleading and conclusions based on such studies may be less robust.
4.2.1.2.4
Experimental designs
In medical studies, researches may be able to restrict systematic reviews to primary ofstudies of one particular type. For example, Cochrane reviews are usually restricted torandomised controlled trials (RCTs). In other circumstances, the nature of thequestion and the central issue being addressed may suggest that certain studies design
are more appropriate than others. However, this approach can only be taken in a
discipline where the amount of available research is a major problem. In software
engineering, the paucity of primary studies is more likely to be the problem forsystematic reviews and we are more likely to need protocols for aggregating
information from studies of widely different types. A starting point for suchaggregation is the ranking of primary studies of different types; this is discussed in
Section 5.3.1.

4.2.2 Protocol Review
The protocol is a critical element of any systematic review. Researchers must agree aprocedure for reviewing the protocol. If appropriate funding is available, a group of
independent experts should be asked to review the protocol. The same experts canlater be asked to review the final report.
PhD students should present their protocol to their supervisors for review andcriticism.

5.

Conducting the review

Once the protocol has been agreed, the review proper can start. This involves:
1. Identification of research
2. Selection of studies
3. Study quality assessment
4. Data extraction and monitoring progress
5. Data synthesis
Each of these stages will be discussed in this section. Although some stages must
proceed sequentially, some stages can be undertaken simultaneously.
5.1

Identification of Research

The aim of a systematic review is to find as many primary studies relating to theresearch question as possible using an unbiased search strategy. For example, it isnecessary to avoid language bias. The rigour of the search process is one factor that
distinguishes systematic reviews from traditional reviews.

5.1.1 Generating a search strategy
It is necessary to determine and follow a search strategy. This should be developed inconsultation with librarians. Search strategies are usually iterative and benefit from:• Preliminary searches aimed at both identifying existing systematic reviews andassessing the volume of potentially relevant studies.

7

•
•
•

Trial searchers using various combinations of search terms derived from theresearch question
Reviews of research results
Consultations with experts in the fieldA general approach is to break down the question into individual facets i.e.
population, intervention, outcomes, study designs. Then draw up a list of synonyms,
abbreviations, and alternative spellings. Other terms can be obtained by considering
subject headings used in journals and data bases. Sophisticated search strings can thenbe constructed using Boolean AND’s and OR’s.
Initial searches for primary studies can be undertaken initially using electronic
databases but this is not sufficient. Other sources of evidence must also be searched(sometimes manually) including:• Reference lists from relevant primary studies and review articles• Journals (including company journals such as the IBM Journal of Research andDevelopment), grey literature (i.e. technical reports, work in progress) andconference proceedings• Research registers• The Internet.
It is also important to identify specific researchers to approach directly for advice on
appropriate source material.
Medical researchers have developed pre-packaged research strategies. Software
Engineering Researchers need to develop and publish such strategies including
identification of relevant electronic databases.

5.1.2 Publication Bias
Publication bias refers to the problem that positive results are more likely to be
published than negative results. The concept of positive or negative results sometimes
depends on the viewpoint of the researcher. (For example, evidence that full
mastectomies were not always required for breast cancer was actually an extremely
positive result for breast cancer sufferers). However, publication bias remains a
problem particularly for formal experiments, where failure to reject the null
hypothesis is considered less interesting than an experiment that is able to reject thenull hypothesis.
Publication bias can lead to systematic bias in systematic reviews unless special
efforts are made to address this problem. Many of the standard search strategies
identified above are used to address this issue including:• Scanning the grey literature• Scanning conference proceedings• Contacting experts and researches working in the area and asking them if theyknow of any unpublished results.
In addition, statistical analysis techniques can be used to identify the potential
significance of publication bias (se Section 5.5.5).

8

5.1.3 Bibliography Management and Document Retrieval
Bibliographic packages such as Reference Manager or Endnote are very useful tomanage the large number of reference that can be obtained from a thorough literature
research.
Once reference lists have been finalised the full articles of potentially useful studies
will need to be obtained. A logging system is needed to make sure all relevant studies
are obtained.

5.1.4 Documenting the Search
The process of performing a systematic review must be transparent and replicable:• The review must be documented in sufficient detail for readers to be able toassess the thoroughness of the search.
• The search should be documented as it occurs and changes noted and justified.
• The unfiltered search results should be saved and retained for possible reanalysis.
Procedures for documenting the search process are given in Table 1.
Table 1 Search process documentation
Data Source
Electronic database

Journal Hand Searches
Conference proceedings

Efforts to identify
unpublished studies
Other sources

5.2

Documentation
Name of database
Search strategy for each database
Date of searchYears covered by search
Name of journalYears searched
Any issues not searched
Title of proceedings
Name of conference (if different)
Title translation (if necessary)
Journal name (if published as part of a journal)
Research groups and researchers contacted (Names and contact details)
Research web sites searched (Date and URL)
Date Searched/Contacted
URL
Any specific conditions pertaining to the search

Study Selection

Once the potentially relevant primary studies have been obtained, they need to beassessed for their actual relevance.

5.2.1 Study selection criteria
Study selection criteria are intended to identify those primary studies that provide
direct evidence about the research question. In order to reduce the likelihood of bias,
selection criteria should be decided during the protocol definition.
Inclusion and exclusion criteria should be based on the research question. They
should be piloted to ensure that they can be reliably interpreted and that they classify
studies correctly.
9Issues:• It is important to avoid, as far as possible, exclusions based on the language of the
primary study. It is often possible to cope with French or German abstracts, but
Japanese or Chinese papers are often difficult to access unless they have a wellstructured English abstract.
• It is possible that inclusion decisions could be affected by knowledge of theauthors, institutions, journals or year of publication. Some medical researchers
have suggested reviews should be done after such information has been removed.
However, it takes time to do this and experimental evidence suggests thatmasking the origin of primary studies does not improve reviews [3].

5.2.2 Study selection process
Study selection is a multistage process. Initially, selection criteria should beinterpreted liberally, so that unless studies identified by the electronic and handsearchers can be clearly excluded based on titles and abstracts, full copies should beobtained.
Final inclusion/exclusion decisions should be made after the full texts have been
retrieved. It is useful to maintain a list of excluded studies identifying the reason for
exclusion.

5.2.3 Reliability of inclusion decisions
When two or more researchers assess each paper, agreement between researchers canbe measured using the Cohen Kappa statistic [6]. Each disagreement must be
discussed and resolved. This may be a matter of referring back to the protocol or may
involve writing to the authors for additional information. Uncertainty about theinclusion/exclusion of some studies should be investigated by sensitivity analysis.
A single researcher should consider discussing included and excluded papers with anexpert panel.
5.3

Study Quality Assessment

In addition, to general inclusion exclusion criteria, it is generally considered important
to assess the “quality” of primary studies:• To provide still more detailed inclusion/exclusion criteria.
• To investigate whether quality differences provide an explanation for differencesin study results.
• As a means of weighting the importance of individual studies when results arebeing synthesised.
• To guide the interpretation of findings and determine the strength of inferences.
• To guide recommendations for further research.
An initial difficulty is that there is no agreed definition of study “quality”. However,
the CRD Guidelines [12] and the Cochrane Reviewers’ Handbook [4] both suggestthat quality relates to the extent to which the study minimises bias and maximisesinternal and external validity (see Table 2).

10Table 2 Quality concept definitions
Term
Bias

Synonyms
Systematic error

Internal validity

Validity

External validity

Generalisability,
Applicability

Definition
A tendency to produce results that depart systematicallyfrom the ‘true’ results. Unbiased results are internally valid
The extent to which the design and conduct of the study arelikely to prevent systematic error. Internal validity is aprerequisite for external validity.
The extent to which the effects observed in the study areapplicable outside of the study.

5.3.1 Quality Thresholds
The CRD Guideline [4] suggests using an assessment of study design to guarantee aminimum level of quality. The Australian National Health and Medical ResearchCouncil guidelines [2] suggest that study design is considered during assessment ofevidence rather than during the appraisal and selection of studies. Both groups
however suggest a hierarchy of study designs (see Table 3 and Table 4).
Table 3 CRD Hierarchy of evidence
Level
1
2
3
3a
3b
4
5

Description
Experimental studies (i.e. RCT with concealed allocation)
Quasi-experimental studies (i.e. studies without randomisation)
Controlled observational studies
Cohort studies
Case control studies
Observational studies without control groups
Expert opinion based on theory, laboratory research or consensus

Table 4 Australian NHMRC Study design hierarchy
Level I
Level II
Level III-1
Level III-2
Level III-3
Level IV

Evidence obtained from a systematic review of all relevant randomised trials
Evidence obtained from at least one properly-designed randomised controlled trial
Evidence obtained from well-designed pseudo-randomised controlled trials (i.e. nonrandom allocation to treatment)
Evidence obtained from comparative studies with concurrent controls and allocation
not randomised, cohort studies, case-control studies or interrupted time series with acontrol group.
Evidence obtained from comparative studies with historical control, two or more
single arm studies, or interrupted time series without a parallel control group
Evidence obtained from case series, either post-test or pretest/post-test

In order to understand Table 3 and Table 4 some additional definitions of studies
types is given in Table 5, where experimental studies are those in which someconditions, particularly those concerning the allocation of participants to different
treatment groups are under the control of investigator and observational studies arethose in which uncontrolled variation in treatment or exposure among study
participants is investigated.
Although the definitions given in Table 5 appear appropriate to software engineeringstudies (replacing the word disease with condition), it is important to note one criticaldifference between medical experiments and software engineering experiments. Most

11

experiments performed in academic settings cannot be equated to randomised
controlled trials (RCTs) in medicine.
Table 5 Definition of study designs
DesignType
Randomised
Controlled
Trial (RCT)
Quasirandomised
trial

Synonym

Basic TypeDefinition

SourceRandomised
Clinical Trial

Experiment[5]

Pseudorandomised
controlled trial

Experiment

Cohortstudy

Follow-up
study,
incidence
study,
longitudinal
study,
prospective
study

Observation

An experiment in which investigators
randomly allocate eligible people into
intervention groups
A study in which the allocation ofparticipants to different intervention
groups is controlled by the investigator but
the method falls short of genuine
randomisation and allocation concealment.
An observational study in which a definedgroup of people (the cohort) is followed
over time. The outcomes of people insubsets are compared to examine forexample people who were exposed to or
not exposed (or exposed at different levels)
to a particular intervention.
A study where a cohort is assembled in thepresent and followed into the future
A study where a cohort is identified frompast records and followed from that timeto the present.
Subjects with the outcome or disease andan appropriate group of controls without
the outcome or disease are selected andinformation is obtained about the previous
exposure to the treatment or other factor
being studied
Outcomes for a prospectively collectedgroup of subjects exposed to a newtreatment/intervention are compared witheither a previously published series orpreviously treated subjects at the same
institutions.
Trends in the outcomes or diseases arecompared over multiple time points before
and after introduction of thetreatment/intervention or other factor being
studied.
Examination of relationships between
diseases and other variables of interest as
they exist in a defined population at one
particular time
A group of subjects are exposed to thetreatment or intervention
A case series where only outcomes after
the intervention are recorded in the caseseries, so no comparisons can be made.
A case series where outcomes aremeasured in subjects before and after
exposure to the treatment/intervention forcomparison.

[12]

Concurrent
cohort study
Historicalcohort study

Observation

Case-control
study

Observation

Historicalcontrol

Observation

Interrupted
time series

Observation

Crosssectional
study

Observation

Case series

Observation

Post-test
case series

Observation

Pre-test /post-test
case series

Observation

Before-andafter study

Observation

[12]

[12]

[12]
[2]

[2]

[2]

[12]

[2]
[2]
[2]

12

RCTs involve real patients with real diseases receiving a new treatment to manage
their condition. That is, RCTs are trials of treatment under its actual use conditions.
The majority of academic experiments involve students doing constrained tasks in
artificial environments. Thus, the major issue for software engineering study
hierarchies is whether small-scale experiments are considered the equivalent oflaboratory experiments and evaluated at the lowest level of evidence, or whether theyshould be ranked higher. In my opinion, they should be ranked higher than expert
opinion. I would consider them equivalent in value to case series or observational
studies without controls. Two other issues that need to be resolved are:• Whether or not systematic reviews are included in the hierarchy.
• Whether or not expert opinion is included in the hierarchy.
The inclusion of systematic reviews depends on whether you are classifying
individual studies or assessing the level of evidence. For assessing individual primarystudies, systematic reviews are, of course, excluded. For assessing the level ofevidence, systematic reviews should be considered the highest level of evidence.
However, in contrast to the implication in the Australian Hierarchy in Table 4, I
believe software engineers must consider systematic reviews of many types ofprimary study not only randomised controlled trials.
The Australian NHMRC guidelines [2] do not included expert opinion in theirhierarchy. The authors remark that the exclusion is a result of studies identifying thefallibility of expert opinion. In software engineering we may have little empiricalevidence, so may have to rely more on expert opinion than medical researchers.
However, we need to recognise the weakness of such evidence.
Table 6 Study design hierarchy for Software Engineering
1
2
3-1
3-2
4-1
4-2
4-3
5

Evidence obtained from at least one properly-designed randomised controlled trial
Evidence obtained from well-designed pseudo-randomised controlled trials (i.e. nonrandom allocation to treatment)
Evidence obtained from comparative studies with concurrent controls and allocation
not randomised, cohort studies, case-control studies or interrupted time series with acontrol group.
Evidence obtained from comparative studies with historical control, two or more
single arm studies, or interrupted time series without a parallel control group
Evidence obtained from a randomised experiment performed in an artificial setting
Evidence obtained from case series, either post-test or pre-test/post-test
Evidence obtained from a quasi-random experiment performed in an artificial setting
Evidence obtained from expert opinion based on theory or consensus

These considerations lead to the hierarchy shown in Table 6 for Software
Engineering. Studies. This table includes reference to randomised controlled trials
although I am aware of only one software engineering experiment that comesanywhere close to a randomised controlled trial in the sense that it undertakes anexperiment in a real-life situation [11]. In this study, Jørgensen and Carelius requested
a bid for a real project from a large number of commercial software companies in
Norway. Companies were selected using stratified random sampling. Once the fullsample was obtained, companies were randomly assigned to two groups. One groupof companies were involved in pre-study phase and the bidding phase, the othercompanies were only involved in the bidding phase. The treatment in this case, wasthe pre-study activity, which involved companies providing an initial non-binding
13

preliminary bid. One aspect that is not consistent with an RCT is that companies were
paid for their time in order to compensate them for providing additional information
to the experimenters. In addition, the study was not aimed at formal hypothesis
testing, so the outcome was a possible explanatory theory rather than a statement of
expected treatment effect.
Normally, primary study hierarchies are used to set a minimum requirement on thetype of study included in the systematic review. In software engineering, we willusually accept all levels of evidence. The only threshold that might be viable would
be to exclude level 5 evidence when there are a reasonable number of primary studies
at a greater level (where a reasonable number must be decided by the researchers, butshould be more than 2).
Categorising evidence hierarchies does not by itself solve the problem of how toaccumulate evidence from studies in different categories. We discuss some fairly
simple ideas in Section 5.5.4 used to present evidence, but we may need to identify
new methods of accumulating evidence from different types of study. For example,
Hardman and Ayton discuss a system to allow the accumulation of qualitative as wellas quantitative evidence in the form arguments that are for or against proposition [13].
In addition, we need better understand the strength of evidence from different types ofstudy. However, this is difficult. For example, there is no agreement among medical
practitioners of the extent to which results from observational studies can really betrusted. Some medical researchers are critical of the reliance on RCTs and reportcases where observational studies produced almost identical results to RCTs [8].
Concato and Horowitz suggest that improvements in reporting clinical conditions (i.e.
collecting more information about individual patients and the reasons for assigning
the patient to a particular treatment) would make observational studies as reliable as
RCTs [7]. In contrast, Lawlor et al. discuss an example where results of a RCT provedthat observational studies were incorrect [14]. Specifically, beneficial effects ofvitamins in giving protection against heart disease found in two observational studies
could not be detected in a randomised controlled trial. They suggest that better
identification and adjustment for possible confounding factors would improve thereliability of observational studies. In addition, Vandenbroucke suggests thatobservational studies are appropriate for detecting negative side-effects, but notpositive side-effects of treatments [17].
Observational studies and experiments in software engineering often have more incommon with studies in the social sciences than medicine. For example, both social
science and software engineering struggle with the problems both of defining andmeasuring constructs of interest, and of understanding the impact of experimental
context on study results. From the viewpoint of social science, Shadish et al. provide a
useful discussion of the study design and analysis methods that can improve thevalidity of experiments and quasi-experiments [16]. They emphasise the importanceof identifying and either measuring or controlling confounding factors. They also
discuss threats to validity across all elements of a study i.e. subjects, treatments,
observations and settings.

14

5.3.2 Development of Quality Instruments
Once the primary studies have been selected a more detailed quality assessment needs
to be made. This allows researchers to assess differences in the executions of studies
within design categories. This information is important for data synthesis andinterpretation of results. Detailed quality assessments are usually based on “quality
instruments” which are checklists of factors that need to be assessed for each study. If
quality items within a checklist are assigned numerical scales numerical assessmentsof quality can be obtained.
Checklists are usually derived from a consideration of factors that could bias study
results. The CRD Guidelines [12], the Australian National Health and Medical
Research Council Guidelines [1], and the Cochrane Reviewers’ Handbook [4] all refer
to four types of bias shown in Table 7. (I have amended the definitions (slightly) andprotection mechanisms (considerably) to address software engineering rather than
medicine.) In particular, medical researchers rely on “blinding” subjects andexperimenters (i.e. making sure that neither the subject nor the researcher knows
which treatment a subject is assigned to) to address performance and measurement
bias. However, that protocol is often impossible for software engineeringexperiments.
Table 7 Types of BiasType
Selection
bias

Synonyms
Allocation
bias

Performancebias

Definition
Systematic difference between
comparison groups with respect totreatment
Systematic difference is the
conduct of comparison groups
apart from the treatment being
evaluated.

Measurement
bias

Detection
Bias

Attrition bias

Exclusion
bias

Systematic difference between thegroups in how outcomes areascertained.
Systematic differences between
comparison groups in terms ofwithdrawals or exclusions ofparticipants from the study
sample.

Protection mechanism
Randomisation of a large numberof subjects with concealment of
the allocation method (e.g.
allocation by computer program
not experimenter choice).
Replication of the studies using
different experimenters.
Use of experimenters with nopersonal interest in either
treatment.
Blinding outcome assessors to thetreatments is sometimes possible.
Reporting of the reasons for all
withdrawals. Sensitivity analysis
including all excluded participants.

The factors identified in Table 7 are refined into a quality instrument by considering:• Generic items that relate to features of particular study designs such as lack ofappropriate blinding, unreliable measurement techniques, inappropriate selectionof subjects, and inappropriate statistical analysis.
• Specific items that relate to the review’s subject area such as use of outcome
measures inappropriate for answering the research question.
More detailed discussion of bias (or threats to validity) from the viewpoint of thesocial sciences rather than medicine can be found in Shadish et al. [16].

15Examples of generic quality criteria for several types of study design are shown in
Table 8. The items were derived from lists in [2] and [12].
If required, researchers may construct a measurement scale for each item. Whatever
form the quality instrument takes, it should be assessed for reliability and usability ina pilot project before being applied to all the selected studies.

5.3.3 Using the Quality Instrument
Quality appraisal of each primary study allows researchers to group studies by quality
prior to any synthesis of results. Researchers can then investigate whether there aresystematic differences between primary studies in different quality groups.
Some researchers have suggested weighting results using quality scores. This idea is
not recommended by any of the medical guidelines.

5.3.4 Limitations of Quality Assessment
Primary studies are often poorly reported, so it may not be possible to determine how
to assess a quality criterion. It is possible to assume that because something wasn’treported, it wasn’t done. This assumption may be incorrect. Researchers shouldattempt to obtain more information from the authors of the study.
Table 8 Example of Quality Criteria
Study type
Cohort studies

Case-control
studiesCase series

Quality criteria
How were subjects chosen for the new intervention?
How were subjects selected for the comparison or control?
Were drop-out rates and reasons for drop-out similar across intervention andunexposed groups?
Does the study adequately control for demographic characteristics, and otherpotential confounding variables in the design or analysis?
Was the measurement of outcomes unbiased (i.e. blinded to treatment group andcomparable across groups)?
Were there exclusions from the analysis?
How were cases defined and selected?
How were controls defined and selected? (I.e. were they randomly selected fromthe source population of the cases)
How comparable are the cases and the controls with respect to potential
confounding factors?
Does the study adequately control for demographic characteristics, and otherpotential confounding variables in the design or analysis?
Was measurement of the exposure to the factor of interest adequate and kept
blinded to the case/control status?
Were all selected subjects included in the analysis?
Were interventions and other exposures assessed in the same way for cases andcontrols?
Was an appropriate statistical analysis used (i.e. matched or unmatched)?
Is the study based on a representative sample from a relevant population?
Are criteria for inclusion explicit?
Were outcomes assessed using objective criteria?

There is limited evidence of relationships between factors that are thought to affectvalidity and actual study outcomes. Evidence suggests that inadequate concealment of16

allocation and lack of double-blinding result in over-estimates of treatment effects,
but the impact of other quality factors is not supported by empirical evidence.
It is possible to identify inadequate or inappropriate statistical analysis, but without
access to the original data it is not possible to correct the analysis. Very often software
data is confidential and cannot therefore be made available to researchers. In somecases, software engineers may refuse to make their data available to other researchers
because they want to continue publishing analyses of the data.
5.4

Data Extraction

The objective of this stage is to design data extraction forms to accurately record theinformation researchers obtain from the primary studies. To reduce the opportunity
for bias, data extraction forms should be defined and piloted when the study protocol
is defined.

5.4.1 Design of Data Extraction Forms
The data extraction forms must be designed to collect all the information needed toaddress the review questions and the study quality criteria. They must also collect alldata items specified in the review synthesis strategy section of the protocol.
In most cases, data extraction will define a set of numerical values that should be
extracted for each study (e.g. number of subjects, treatment effect, confidence
intervals, etc.). Numerical data are important for any attempt to summarise the resultsof a set of primary studies and are a prerequisite for meta-analysis (i.e. statisticaltechniques aimed at integrating the results of the primary studies).
Data extraction forms need to be piloted on a sample of primary studies. If several
researchers will use the forms, several researchers should take part in the pilot. The
pilot studies are intended to assess both technical issues such as the completeness ofthe forms and usability issues such as the clarity of user instructions and the orderingof questions.
Electronic forms are useful and can facilitate subsequent analysis.

5.4.2 Contents of Data Collection Forms
In addition, to including all the questions needed to answer the review question andquality evaluation criteria, data collection forms should provide standard information
including:• Name of Review• Date of Data extraction• Title, authors, journal, publication details• Space for additional notes

5.4.3 Data extraction procedures
Whenever feasible, data extraction should be performed independently by two or
more researchers. Data from the researchers must be compared and disagreements
resolved either by consensus among researchers or arbitration by an additional
independent researcher. Uncertainties about any primary sources for which agreement17

cannot be reached should be investigated as part of any sensitively analyses. A
separate form must be used to mark and correct errors or disagreements.
If several researchers each review different primary studies because time or resource
constraints prevent all primary papers being assessed by at least two researchers, it isimportant to ensure employ some method of checking that researchers extract data in
a consistent manner. For example, some papers should be reviewed by all researchers(e.g. a random sample of primary studies), so that inter-researcher consistency can beassessed.
For single researchers such as PhD students, other checking techniques must be used,
for example supervisors should be asked to perform data extraction on a random
sample of the primary studies and results cross-checked with those of the student.

5.4.4 Multiple publications of the same data
It is important to avoid including multiple publications of the same data in asystematic review synthesis because duplicate reports would seriously bias any
results. It may be necessary to contact the authors to confirm whether or not reports
refer to the same study. When there are duplicate publications, the most recent should
be used.

5.4.5 Unpublished data, missing data and data requiring
manipulation
If information is available from studies in progress, it should be included providing
appropriate quality information about the study can be obtained and written
permission is available from the researchers.
Reports do not always include all relevant data. They may also be poorly written andambiguous. Again the authors should be contacted to obtain the required information.
Sometimes primary studies do not provide all the data but it is possible to recreate therequired data by manipulating the published data. If any such manipulations are
required, data should first be reported in the way they were reported. Data obtained bymanipulation should be subject to sensitivity analysis.
5.5

Data Synthesis

Data synthesis involves collating and summarising the results of the included primarystudies. Synthesis can be descriptive (non-quantitative). However, it is sometimes
possible to complement a descriptive synthesis with a quantitative summary. Using
statistical techniques to obtain a quantitative synthesis is referred to as meta-analysis.
Description of meta-analysis methods is beyond the scope of this document, althoughtechniques for displaying quantitative results will be described. (To learn more aboutmeta-analysis see [4].)The data synthesis activities should be specified in the review protocol. However,some issues cannot be resolved until the data is actually analysed, for example, subset
analysis to investigate heterogeneity is not required if the results show no evidence ofheterogeneity.

18

5.5.1 Descriptive synthesis
Extracted information about the studies (i.e. intervention, population, context, sample
sizes, outcomes, study quality) should be tabulated in a manner consistent with thereview question. Tables should be structured to highlight similarities and difference
between study outcomes.
It is important to identify whether results from studies are consistent one with another(i.e. homogeneous) or inconsistent (e.g. heterogeneous). Results may be tabulated todisplay the impact of potential sources of heterogeneity, e.g. study type, study quality,
and sample size.
Quantitative data should also be presented in tabular form including:• Sample size for each intervention• Estimates effect size for each intervention with standard errors for each effect• Difference between the mean values for each intervention, and the confidence
interval for the difference.
• Units used for measuring the effect.

5.5.2 Quantitative Synthesis
To synthesis quantitative results from different studies, study outcomes must bepresented in a comparable way. Medical guidelines suggest different effect measuresfor different types of outcome.
Binary outcomes (Yes/No, Success/Failure) can be measured in several different
ways:• Odds. The ratio of the number of subjects in a group with an event to the number
without an event. Thus if 20 projects in a group of 100 project failed to achievebudgetary targets, the odds would be 20/80 or 0.25.
• Risk (proportion, probability, rate) The proportion of subjects in a group observed
to have an event. Thus, if 20 out of 100 projects failed to achieve budgetary
targets, the risk would be 20/100 or 0.20.
• Odds ratio (OR). The ratio of the odds of an event in the experimental (orintervention) group to the odds of an event on the control group. An OR equal to
one indicates no difference between the control and the intervention group. For
undesirable outcomes a value less than one indicates that the intervention was
successful in reducing risk, for a desirable outcome a value greater than one
indicates that the intervention was successful in reducing risk.
• Relative risk (RR) (risk ratio, rate ratio). The ratio of risk in the intervention
group to the risk in the control group. An RR of one indicates no difference
between comparison groups. For undesirable events an RR less than one indicatesthe intervention was successful, for desirable events an RR greater than one
indicates the intervention was successful.
• Absolute risk reduction (ARR) (risk difference, rate difference). The absolutedifference in the event rate between the comparison groups. A difference of zero
indicates no difference between the groups. For an undesirable outcome an ARRless than zero indicates a successful intervention, for a desirable outcome an ARR
greater than zero indicates a successful intervention.

19

Each of these measures has advantages and disadvantages. For example, odds andodds ratios are criticised for not being well-understood by non-statisticians (other thangamblers), whereas risk measures are generally easier to understand. Alternatively
statisticians prefer odd ratios because they have some mathematically desirable
properties. Another issue is the relative measures are generally more consistent thanabsolute measures for statistical analysis, but decision makers need absolute values inorder to assess the real benefit of an intervention.
Effect measures for continuous data include:• Mean difference. The difference between the means of each group (control andintervention group).
• Weighted mean difference (WMD). When studies have measured the difference
on the same scale, the weight give to each study is usually the inverse of the study
variance• Standardised mean difference (SMD). A common problem when summarisingoutcomes is that outcomes are often measured in different ways, for example,
productivity might be measured in function points per hour, or lines of code perday. Quality might be measured as the probability of exhibiting one or more faults
or the number of faults observed. When studies use different scales, the meandifference may be divided by an estimate of the within-groups standard deviation
to produce a standardised value without any units. However, SMDs are only valid
if the difference in the standard deviations reflect differences in the measurement
scale, not real differences among trial populations.

5.5.3 Presentation of Quantitative Results
The most common mechanism for presenting quantitative results is a forest plot, asshown in Figure 1. A forest plot presents the means and variance for the difference foreach study. The line represents the standard error of the difference, the box represents
the mean difference and its size is proportional to the number of subjects in the study.
A forest plot may also be annotated with the numerical information indicating thenumber of subjects in each group, the mean difference and the confidence interval on
the mean. If a formal meta-analysis is undertaken, the bottom entry in a forest plot
will be the summary estimate of the treatment difference and confidence interval forthe summary difference.
Figure 1 represents the ideal result of a quantitative summary, the results of thestudies basically agree. There is clearly a genuine treatment effect and a single overallsummary statistics would be a good estimate of that effect. If effects were very
different from study to study, our results would suggest heterogeneity. A single
overall summary statistics would probably be of little value. The systematic review
should continue with an investigation of the reasons for heterogeneity. To avoid the
problems of post-hoc analysis, researchers should identify possible sources ofheterogeneity when they construct the review protocol.

20

Figure 1 Example of a forest plot
Study 1
Study 2

Study 3

-0.2

-0.1

Favours control

0

0.1

0.2

Favours intervention

5.5.4 Sensitivity analysis
Sensitivity analysis is much more important when a full meta-analysis is performed
than when no formal meta-analysis is performed. Meta-analysis is used to provide an
overall estimate of the treatment effect and its variability. In such cases, the results of
the analysis should be repeated on various subsets of primary studies to determine
whether the results are robust. The types of subsets selected would be:• High quality primary studies only.
• Primary studies of particular types.
• Primary studies for which data extraction presented no difficulties (i.e. excluding
any studies where there was some residual disagreement about the data extracted).
When a formal meta-analysis is not undertaken, forest plots can be annotated to
identify high quality primary studies, the studies can be presented in decreasing orderof quality or in decreasing study type hierarchy order. Primary studies where there are
queries about the data extracted can also be explicitly identified on the forest plot, byfor example, using grey colouring for less reliability studies and black colouring for
reliable studies.

5.5.5 Publication bias
Funnel plots are used to assess whether or not a systematic review is likely to bevulnerable to publication bias. Funnel plots plot the treatment effect (i.e. meandifference between intervention group and control) against the inverse of the varianceor the sample size. A systematic review that exhibited the funnel shape shown in
Figure 2 would be assumed not to be exhibiting evidence of publication bias. It would
be consistent with studies based on small samples showing more variability inoutcome than studies based on large samples. If, however, the points shown as filledin black dots were not present, the plot would be asymmetric and it would suggest thepresence of publication bias. This would suggest the results of the systematic survey
must be treated with caution.

21

Figure 2 An example of a funnel plot
1/variance

Treatment effect6.

Reporting the review

It is important to communicate the results of a systematic review effectively. Usuallysystematic reviews will be reported in at least two formats:1.
In a technical report or in a section of a PhD thesis.
2.
In a journal or conference paper.
A journal or conference paper will normally have a size restriction. In order to ensurethat readers are able to properly evaluate the rigour and validity of a systematic
review, journal papers should reference a technical report or thesis that contains all
the details.
In addition, systematic reviews with important practical results may be summarised in
non-technical articles in practitioner magazines, in press releases and in Web pages.
6.1

Structure for systematic review

The structure and contents of reports suggested in [12] is presented in Table 9. This
structure is appropriate for technical reports and journals. For PhD theses, the entries
marked with an asterisk are not likely to be relevant.
6.2

Peer Review

Journal articles will be peer reviewed as a matter of course. Experts review PhD
theses as part of the examination process. In contrast, technical reports are not usuallysubjected to peer review. However, if systematic reviews are made available on the
Web so that results are made available quickly to researchers and practitioners, it isworth organising a peer review. If an expert panel were assembled to review the study
protocol, the same panel would be appropriate to undertake peer review of thesystematic review report.
.

22

Table 9 Structure and contents of reports of systematic reviews
SectionTitle*

Subsection

Scope

Context

The importance of the researchquestions addressed by the review
The questions addressed by thesystematic review
Data Sources, Study selection, Quality
Assessment and Data extraction
Main finding including any metaanalysis results and sensitivity
analyses.
Implications for practice and future
research
Justification of the need for the review.
Summary of previous reviews

Authorship*

Executive summary
or Structured
Abstract*

Objectives
Methods
Results
Conclusions

Background
Review questions
Review Methods

Included andexcluded studies

Comments
The title should be short but informative. It should be based on the question
being asked. In journal papers, it should indicate that the study is asystematic review.
When research is done collaboratively, criteria for determining both whoshould be credited as an author, and the order of author’s names should be
defined in advance. The contribution of workers not credited as authors
should be noted in the Acknowledgements section.
A structured summary or abstract allows readers to assess quickly therelevance, quality and generality of a systematic review.

Description of the software engineering technique being investigated and its
potential importance

Each review question should bespecified

Identify primary and secondary review questions. Note this section may be
included in the background section.
This should be based on the research protocol. Any changes to the original
protocol should be reported.

Inclusion and exclusion criteria
List of excluded studies with rationale
for exclusion

Study inclusion and exclusion criteria can sometimes best be represented as aflow diagram because studies will be excluded at different stages in thereview for different reasons.

Data sources and searchstrategy
Study selection
Study quality assessment
Data extraction
Data synthesis

23

Results

Findings

Discussion

Sensitivity analysis
Principal findings
Strengths and Weaknesses

Meaning of findings

Conclusions

Acknowledgements*
Conflict of Interest
References andAppendices

Recommendations

Description of primary studies
Results of any quantitative summaries
Details of any meta-analysis
Strength and weaknesses of theevidence included in the review
Relation to other reviews, particularly
considering any differences in quality
and results.
Direction and magnitude of effectobserved in summarised studies
Applicability (generalisability) of thefindings

Practical implications for softwaredevelopment
Unanswered questions and implications
for future research
All persons who contributed to theresearch but did fulfil authorshipcriteria

Non-quantitative summaries should be provided to summarise each of thestudies and presented in tabular form.
Quantitative summary results should be presented in tables and graphs
These must correspond to the findings discussed in the results section
A discussion of the validity of the evidence considering bias in thesystematic review allows a reader to assess the reliance that may be placedon the collected evidence.
Make clear to what extent the result imply causality by discussing the levelof evidence.
Discuss all benefits, adverse effects and risks.
Discuss variations in effects and their reasons (for example are the treatment
effects larger on larger projects).
What are the implications of the results for practitioners?

Any secondary interest on the part of the researchers (e.g. a financial interestin the technology being evaluated) should be declared.
Appendices can be used to list studies included and excluded from the study,
to document search strategy details, and to list raw data from the included
studies.

24

7. Final remarks
This report has presented a set of guidelines for planning conducting and reporting
systematic review. The guidelines are based on guidelines used in medical research.
However, it is important to recognise that software engineering research is not thesame as medical research. We do not undertake randomised clinical trials, nor can weuse blinding as a means to reduce distortions due to experimenter and subject
expectations. Thus, software engineering research studies usually provide only weakevidence compared with RCTs.
We need to consider mechanisms to aggregate evidence from studies of different
types and to understand the extent to which we can rely on such evidence. At present,
these guidelines merely suggest that data from primary studies should beaccompanied by information about the type of primary study and its quality. As yet,there is no definitive method for accumulating evidence from studies of different
types. Furthermore, there is disagreement among medical researchers about how
much reliance can be placed on evidence from studies other than RCTs. However, thelimited number of primary studies in software engineering imply that it is critical toconsider evidence from all types of primary study, including laboratory/academic
experiments, and as well as evidence obtained from experts.
Finally, these guidelines are intended to assist PhD students as well as larger researchgroups. However, many of the steps in a systematic review assume that it will be
undertaken by a large group of researchers. In the case of a single research (such as
PhD student), we suggest the most important steps to undertake are:• Developing a protocol.
• Defining the research question.
• Specifying what will be done to address the problem of a single researcher
applying inclusion/exclusion criteria and undertaking all the data extraction.
• Defining the search strategy.
• Defining the data to be extracted from each primary study including quality data.
• Maintaining lists of included and excluded studies.
• Using the data synthesis guidelines.
• Using the reporting guidelines

8.

References[1]

Australian National Health and Medical Research Council. How to review theevidence: systematic identification and review of the scientific literature, 2000.
IBSN 186-4960329.
Australian National Health and Medical Research Council. How to use theevidence: assessment and application of scientific evidence. February 2000,
ISBN 0 642 43295 2.
Berlin, J.A., Miles, C.G., Crigliano, M.D. Does blinding of readers affect theresults of meta-analysis? Online J. Curr. Clin. Trials, 1997: Doc No 205.
Cochrane Collaboration. Cochrane Reviewers’ Handbook. Version 4.2.1.
December 2003

[2]
[3]
[4]

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[5]
[6]
[7]
[8]
[9]
[10]

[11]
[12]

[13]
[14]

[15]

[16]
[17]

Cochrane Collaboration. The Cochrane Reviewers’ Handbook Glossary,
Version 4.1.5, December 2003.
Cohen, J. Weighted Kappa: nominal scale agreement with provision for scaled
disagreement or partial credit. Pychol Bull (70) 1968, pp. 213-220.
Concato, John and Horowitz, Ralph. I. Beyond randomised versus observational
studies. The Lancet, vol363, Issue 9422, 22 May, 2004.
Feinstein, A.R., and Horowitz, R.I. Problems with the “evidence” of “evidencebased medicine”. Ann. J. Med., 1977, vol(103) pp529-535.
Greenlaugh, Trisha. How to read a paper: Papers that summarise other papers(systematic reviews and meta-analysies. BMJ, 315, 1997, pp. 672-675.
Jasperson, Jon (Sean), Butler, Brian S., Carte, Traci, A., Croes, Henry J.P.,
Saunders, Carol, S., and Zhemg, Weijun. Review: Power and Information
Technology Research: A Metatriangulation Review. MIS Quarterly, 26(4): 397459, December 2002.
Jørgensen, Magne and Carelius, Gunnar J. An Empirical Study of Software
Project Bidding, Submitted to IEEE TSE, 2004 (major revision required).
http://www.simula.no/photo/desbidding16.pdf.
Khan, Khalid, S., ter Riet, Gerben., Glanville, Julia., Sowden, Amanda, J. andKleijnen, Jo. (eds) Undertaking Systematic Review of Research on
Effectiveness. CRD’s Guidance for those Carrying Out or Commissioning
Reviews. CRD Report Number 4 (2nd Edition), NHS Centre for Reviews andDissemination, University of York, IBSN 1 900640 20 1, March 2001.
Hardman, David, K, and Ayton, Peter. Arguments for qualitative risk
assessment: the StAR risk advisor. Expert Systems, Vol 14, No. 1., 1997, pp2436.
Lawlor, Debbie A., George Davey Smith, K Richard Bruckdorfer, Devi Kundu,
Shah. Ebrahim Those confounded vitamins: what can we learn from thedifferences between observational versus randomised trial evidence? The
Lancet, vol363, Issue 9422, 22 May, 2004.
Pai, Madhukar., McCulloch, Michael., and Colford, Jack. Systematic Review: A
Road Map Version 2.2. Systematic Reviews Group, UC Berkeley, 2002.
[www.medepi.org/meta/guidelines/Berkeley_Systematic_Reviews_Road_Map_
V2.2.pdf viewed 20 June 2004].
Shadish, W.R., Cook, Thomas, D. and Campbell, Donald, T. Experimental andQuasi-experimental Designs for Generalized Causal Inference. Houghton
Mifflin Company, 2002.
Jan P Vandenbroucke. When are observational studies as credible as
randomised trials? The Lancet, vol363, Issue 9422, 22 May, 2004.

26

Appendix 1

Steps in a systematic review

Guidelines for systematic review in the medical domain have different view of theprocess steps needed in a systematic review. The Systematic Reviews Group (UCBerkely) present a very detailed process model [15], other sources present a coarser
process. These process steps are summarised in Table 10, which also attempts tocollate the different processes.
Pai et al. [15] have specified the review process steps at a more detailed level of
granularity than the other systematic review guidelines. In particular, they have madeexplicit the iterative nature of the start of a systematic review process. The start-up
problem is not discussed in any of the other guidelines. However, it is clear that it isdifficult to determine the review protocol without any idea as to the nature of theresearch question and vice-versa.
Table 10 Systematic review process proposed in different guidelines
Systematic Reviews Group([15])

Define the question &develop draft protocol
Identify a few relevant
studies and do a pilot study;
specific inclusion/exclusion
criteria, test forms andrefine protocol.
Identify appropriate
databases/sources.
Run searches on all relevant
data bases and sources.
Save all citations(titles/abstracts) in areference manager.
Document search strategy.
Researchers (at least 2)
screen titles & abstracts.
Researchers meet & resolve
differences.
Get full texts of all articles.
Researchers do second
screen.
Articles remaining after
second screen is the final set
for inclusion
Researchers extract data
including quality data.

Australian National
Health and Medical
Research Council ([1])

Cochrane
Reviewers
Handbook ([4])

Developing a
protocolCRD Guidance ([12])Identification of theneed for a review.
Preparation of a
proposal for asystematic review
Development of areview protocol

Question Formulation

Formulating the
problemFinding Studies

Locating andselecting studies forreviews

Identification ofresearch
Selection of studies

Appraisal and selectionof studies

Assessment ofstudy quality

Study quality
assessment

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Researchers meet to resolve
disagreements on data
Compute inter-rater
reliability.
Enter data into databasemanagement software
Import data and analyseusing meta-analysis
software.
Pool data if appropriate.
Look for heterogeneity.
Interpret & present data.
Discuss generalizability ofconclusions and limitationsof the review.
Make recommendations forpractice or policy, &research.

Collecting data

Data extraction &monitoring progress

Summary and synthesisof relevant studies

Analysing &presenting results

Data synthesis

Determining theapplicability of results.
Reviewing andappraising the
economics literature.

Interpreting theresults

The report andrecommendations.
Getting evidence into
practice.

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