SOFTWARE TESTING, VERIFICATION AND RELIABILITY
Softw. Test. Verif. Reliab. 2007; 00:1–7 (DOI: 10.1002/000)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/000

Regression Testing
Minimisation, Selection andPrioritisation : A Survey
S. Yoo, M. Harman
King’s College London, Centre for Research on Evolution, Search & Testing,
Strand, London, WC2R 2LS, UK

SUMMARY
Regression testing is a testing activity that is performed to provide confidence that changes do not harmthe existing behaviour of the software. Test suites tend to grow in size as software evolve, often making it
too costly to execute entire test suites. A number of different approaches have been studied to maximise
the value of the accrued test suite: minimisation, selection and prioritisation. Test suite minimisation seeks
to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks
to identify the test cases that are relevant to some set of recent changes. Test case prioritisation seeks toorder test cases in such a way that early fault detection is maximised. This paper surveys each area ofminimisation, selection and prioritisation technique and discusses open problems and potential directions
for future research.
Copyright c 2007 Shin Yoo & Mark Harman
Received 23 September 2007; Revised 24 September 2007
KEY WORDS :1.

Regression Testing, Test Suite Minimisation, Regression Test Selection, Test Case Prioritisation

Introduction

Regression testing is performed when changes are made to existing software; the purpose of regression
testing is to provide confidence that the newly introduced changes do not obstruct the behaviours of
the existing, unchanged part of the software. It is a complex procedure that is all the more challenging
because of some of the recent trends in software development paradigms. For example, the componentbased software development method tends to result in use of many black-box components, often
adopted from a third-party. Any change in the third-party components may interfere with the rest of
the software system, yet it is hard to perform regression testing because the internals of the third-party
components are not known to their users. The shorter life-cycle of software development, such as theContract/grant sponsor: Your sponsor; contract/grant number:

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one suggested by the agile programming discipline, also imposes restrictions and constraints on how
regression testing can be performed within limited resources.
Naturally, the most straightforward approach to this problem is to simply execute all the existing
test cases in the test suite; this is called a retest-all approach. However, as software evolves, the test
suite tends to grow, which means it may be prohibitively expensive to execute the entire test suite.
This limitation forces consideration of techniques that seek to reduce the effort required for regression
testing in various ways.
A number of different approaches have been studied to aid the regression testing process. The threemajor branches include test suite minimisation, test case selection and test case prioritisation. Test
suite minimisation is a process that seeks to identify and then eliminate the obsolete or redundant test
cases from the test suite. Test case selection deals with the problem of selecting a subset of test casesthat will be used to test the changed parts of the software. Finally, test case prioritisation concerns theidentification of the ‘ideal’ ordering of test cases that maximises desirable properties, such as early fault
detection. Existing empirical studies show that the application of these techniques can be cost-effective.
This paper surveys work undertaken in these three related branches of regression testing. Section 2
introduces the nomenclature. Section 3 describes different test suite minimisation techniques as wellas their efficiency and effectiveness. Section 4 examines test case selection techniques according to
the specific analysis technique used, and evaluates the strengths and weaknesses of each approach.
Section 5 introduces test case prioritisation techniques. Section 6 introduces meta-empirical studies
concerning evaluation methodologies and cost-effectiveness analysis of regression testing techniques.
Section 7 presents a summary of the field and identifies trends and issues. Section 8 introducessome gaps in the existing literature, thereby suggesting potential directions for future work. Section 9
concludes.
1.1.

Motivation

When writing a survey paper there are two natural questions that need to be asked:
1. Why is this the right set of topics for a survey?
2. Is there already a recent survey in this area?
The first question concerns the motivation for the scope chosen for the survey, while the second
concerns the perceived need for such a survey, once a suitable scope is established. For this paper thescope has been chosen to include topics on test suite minimisation, Regression Test Selection (RTS)
and test case prioritisation. The reason for the choice of this scope is that these three topics are related
by a common thread of optimisation; each is an approach that optimises the application of an existing
pool of test cases.
The fact that all three approaches assume the existence of a pool of test cases distinguishes these
topics from test case generation, which seeks to create pools of test data. The three topics form a
coherent set of approaches, each of which shares a common starting point; that the tester has a pool oftest cases that is simply too large to allow all cases to be applied to the System Under Test (SUT). Eachof the three approaches denotes a different way of coping with this problem of scale.
The relationship between the three techniques goes deeper than the mere shared application to poolsof test data. It is not only the sets of problems addressed by each that exhibit overlap, but also the

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solution approaches that are applied. There is an intimate relationship between solutions to the three
related problems, as this survey reveals. For instance, one way of selecting (or minimising) a set of ntest cases from a test pool would be to prioritise the whole set and pick the first n in priority order. Of
course, there may be more optimal choices, since prioritisation has to contend with any possible choiceof n, whereas selection merely requires that an optimal choice is found for a given value of n.
Turning to the second question, the closest previous papers to this survey are a survey of Regression
Test Selection (RTS) techniques undertaken in 1996 [1] and a recent systematic review on RTS [2] thatconcerns a specific set of research questions rather than a survey of the whole area. No previous survey
on regression testing considered minimisation, selection and prioritisation collectively. The present
survey claims that these classes of techniques are closely related to each other and denote a coherent
sub-area of study. Our survey also includes recent applications of techniques that were surveyed in theearlier work of Rothermel and Harrold [1]. There are existing comparative studies on RTS [3–7], butthese papers only concern a small number of specific RTS techniques and do not provide an overview
of the field.
1.2.

Selection of Papers

This survey aims to collect and consider papers that deal with three regression testing techniques:
test suite minimisation, regression test selection and test case prioritisation. Our intention is not toundertake a systematic review, but rather to provide a broad state-of-the-art view on these related
fields. Many different approaches have been proposed to aid regression testing, which has resulted ina body of literature that is spread over a wide variety of domains and publication venues. The majorityof surveyed literature has been published in the software engineering domain, and especially in thesoftware testing and software maintenance literature. However, the regression testing literature also
overlaps with those of programming language analysis, empirical software engineering and software
metrics.
Therefore, the paper selection criteria on which this survey is based are the problems consideredin papers, while focusing on the specific topics of minimisation, selection and prioritisation. The
formal definitions of these problems are presented in Section 2.2. The selected papers are listed in
the Appendix. Fast abstracts and short papers have been excluded.

2.

Background

This section introduces the basic concepts and definitions that form a nomenclature of regression
testing and minimisation, selection and prioritisation techniques.
2.1.

Regression Testing

Regression testing is performed between two different versions of software in order to provide
confidence that the newly introduced features of the System Under Test (SUT) do not interfere with theexisting features. While the exact details of the modifications made to SUT will often be available, they
may not be easily available in some cases. For example, when the new version is written in a different
programming language or when the source code is unavailable, modification data will be unavailable.

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The following notations are used to describe concepts in the context of regression testing. Let P be
the current version of the program under test, and P 0 be the next version of P . Let S be the current setof specifications for P , and S 0 be the set of specifications for P 0 . T is the existing test suite. Individual
test cases will be denoted by lower case: t. P (t) stands for the execution of P using t as input.
2.2.

Distinction between Classes of Techniques

It is necessary at this point to establish a clear terminological distinction between the different classesof techniques described in the paper. Test suite minimisation techniques seek to reduce the size ofa test suite by eliminating redundant test cases from the test suite. Minimisation is sometimes also
called ‘test suite reduction’, meaning that the elimination is permanent. However, these two concepts
are essentially interchangeable because all reduction techniques can be used to produce a temporary
subset of the test suite, whereas any minimisation techniques can be used to permanently eliminate test
cases. More formally, following Rothermel et al. [8], the test suite minimisation is defined as follows:
Definition 1. Test Suite Minimisation Problem
Given: A test suite, T , a set of test requirements {r1 , . . . , rn }, that must be satisfied to provide thedesired ‘adequate’ testing of the program, and subsets of T , T1 , . . . , Tn , one associated with each of
the ri s such that any one of the test cases tj belonging to Ti can be used to achieve requirement ri .
Problem: Find a representative set, T 0 , of test cases from T that satisfies all ri s.
The testing criterion is satisfied when every test requirement in {r1 , . . . , rn } is satisfied. A test
requirement, ri , is satisfied by any test case, tj , that belongs to the Ti , a subset of T . Therefore, therepresentative set of test cases is the hitting set of the Ti s. Furthermore, in order to maximise the effectof minimisation, T 0 should be the minimal hitting set of the Ti s. The minimal hitting set problem is an
NP-complete problem as is the dual problem of the minimal set cover problem [9].
While test case selection techniques also seek to reduce the size of a test suite, the majority of
selection techniques are modification-aware. That is, the selection is not only temporary (i.e. specificto the current version of the program), but also focused on the identification of the modified partsof the program. Test cases are selected because they are relevant to the changed parts of the SUT,
which typically involves a white-box static analysis of the program code. Throughout this survey, themeaning of ‘test case selection problem’ is restricted to this modification-aware problem. It is also often
referred to as the Regression Test case Selection (RTS) problem. More formally, following Rothermeland Harrold [1], the selection problem is defined as follows (refer to Section 4 for more details on how
the subset T 0 is selected):Definition 2. Test Case Selection Problem
Given: The program, P , the modified version of P , P 0 , and a test suite, T .
Problem: Find a subset of T , T 0 , with which to test P 0 .
Finally, test case prioritisation concerns ordering test cases for early maximisation of some desirable
properties, such as the rate of fault detection. It seeks to find the optimal permutation of the sequence of

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test cases. It does not involve selection of test cases, and assumes that all the test cases may be executedin the order of the permutation it produces, but that testing may be terminated at some arbitrary point
during the testing process. More formally, the prioritisation problem is defined as follows:
Definition 3. Test Case Prioritisation Problem
Given: a test suite, T , the set of permutations of T , P T , and a function from P T to real numbers,
f : P T → R.
Problem: to find T 0 ∈ P T such that (∀T 00 )(T 00 ∈ P T )(T 00 6= T 0 )[f (T 0 ) ≥ f (T 00 )].
This survey focuses on papers that consider one of these three problems. Throughout the paper, thesethree techniques will be collectively referred to as ‘regression testing techniques’.
2.3.

Classification of Test Cases

Leung and White present the first systematic approach to regression testing by classifying typesof regression testing and test cases [10]. Regression testing can be categorised into progressive
regression testing and corrective regression testing. Progressive regression testing involves changesof specifications in P 0 , meaning that P 0 should be tested against S 0 . On the other hand, corrective
regression testing does not involve changes in specifications, but only in design decisions and actual
instructions. It means that the existing test cases can be reused without changing their input/output
relation.
Leung and White categorise test cases into five classes. The first three classes consist of test casesthat already exist in T .
• Reusable: reusable test cases only execute the parts of the program that remain unchanged
between two versions, i.e. the parts of the program that are common to P and P 0 . It is
unnecessary to execute these test cases in order to test P 0 ; however, they are called reusable
because they may still be retained and reused for the regression testing of the future versions ofP.
• Retestable: retestable test cases execute the parts of P that have been changed in P 0 . Thus
retestable test cases should be re-executed in order to test P 0 .
• Obsolete: test cases can be rendered obsolete because 1) their input/output relation is no longer
correct due to changes in specifications, 2) they no longer test what they were designed to test
due to modifications to the program, or 3) they are ‘structural’ test cases that no longer contributeto structural coverage of the program.
The remaining two classes consist of test cases that have yet to be generated for the regression testingof P 0 .
• New-structural: new-structural test cases test the modified program constructs, providing
structural coverage of the modified parts in P 0 .
• New-specification: new-specification test cases test the modified program specifications, testing
the new code generated from the modified parts of the specifications of P 0 .

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Leung and White go on to propose a retest strategy, in which a test plan is constructed based on theidentification of changes in the program and classification of test cases. Although the definition of a test
plan remains informal, it provides a basis for the subsequent literature. It is of particular importanceto regression test case selection techniques, since these techniques essentially concern the problem ofidentifying retestable test cases. Similarly, test suite minimisation techniques concern the identificationof obsolete test cases. Test case prioritisation also can be thought of as a more sophisticated approachto the construction of a test plan.
It should be noted that the subsequent literature focusing on the idea of selecting and reusing test
cases for regression testing is largely concerned with corrective regression testing only. For progressive
regression testing, it is very likely that new test cases are required in order to test the new specifications.
So far, this aspect of the overall regression testing picture has been a question mainly reserved for test
data generation techniques. However, the early literature envisages a ‘complete’ regression testing
strategy that should also utilise test data generation techniques.

3.

Test Suite Minimisation

Test suite minimisation techniques aim to identify redundant test cases and to remove them from the test
suite in order to reduce the size of the test suite. The minimisation problem described by Definition 1
can be considered as the minimal hitting set problem.
Note that the minimal hitting set formulation of the test suite minimisation problem depends on theassumption that each ri can be satisfied by a single test case. In practice, this may not be true. For
example, suppose that the test requirement is functional rather than structural and, therefore, requires
more than one test case to be satisfied. The minimal hitting set formulation no longer applies. In order
to apply the given formulation of the problem, the functional granularity of test cases needs to be
adjusted accordingly. The adjustment process may be either that a higher level of abstraction would be
required so that each test case requirement can be met with a single test scenario composed of relevant
test cases, or that a ‘large’ functional requirement needs to be divided into smaller sub-requirementsthat will correspond to individual test cases.
3.1.

Heuristics

The NP-completeness of the test suite minimisation problem encourages the application of heuristics;
previous work on test case minimisation can be regarded as the development of different heuristics forthe minimal hitting set problem [11–14].
Horgan and London applied linear programming to the test case minimisation problem in theirimplementation of a data-flow based testing tool, ATAC [13,15]. Harrold et al. presented a heuristic forthe minimal hitting set problem with the worst case execution time of O(|T | ∗ max(|Ti |)) [12]. Here|T | represents the size of the original test suite, and max(|Ti |) represents the cardinality of the largest
group of test cases among T1 , . . . , Tn .
Chen and Lau applied GE and GRE heuristics and compared the results to that of the HGS (HarroldGupta-Soffa) heuristic [11]. The GE and GRE heuristics can be thought of as variations of the greedy
algorithm that is known to be an effective heuristic for the set cover problem [16]. Chen et al. definedessential test cases as the opposite of redundant test cases. If a test requirement ri can be satisfied

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Test Case

t1
t2
t3
t4
t5

7

Testing Requirements
r1

r2

r3

x
x

x

x

r4

r5

r6

x
x

x
x

x
x

Table I. Example test suite taken from Tallam and Gupta [18]. The early selection made by thegreedy approach, t1 , is rendered redundant by subsequent selections, {t2 , t3 , t4 }.

by one and only one test case, the test case is an essential test case. On the other hand, if a test casesatisfies only a subset of the test requirements satisfied by another test case, it is a redundant test case.
Based on these concepts, the GE and GRE heuristics can be summarised as follows:• GE heuristic: first select all essential test cases in the test suite; for the remaining test
requirements, use the additional greedy algorithm, i.e. select the test case that satisfies the
maximum number of unsatisfied test requirements.
• GRE heuristic: first remove all redundant test cases in the test suite, which may make some test
cases essential; then perform the GE heuristic on the reduced test suite.
Their empirical comparison suggested that no single technique is better than the other. This is anatural finding, because the techniques concerned are heuristics rather than precise algorithms.
Offutt et al. also treated the test suite minimisation problem as the dual of the minimal hitting set
problem, i.e., the set cover problem [14]. Their heuristics can also be thought of as variations of thegreedy approach to the set cover problem. However, they adopted several different test case orderings,
instead of the fixed ordering in the greedy approach. Their empirical study applied their techniques toa mutation-score-based test case minimisation, which reduced sizes of test suites by over 30%.
Whereas other minimisation approaches primarily considered code-level structural coverage, Marré
and Bertolino formulated test suite minimisation as a problem of finding a spanning set over agraph [17]. They represented the structure of the SUT using a decision-to-decision graph (ddgraph).
A ddgraph is a more compact form of the normal CFG since it omits any node that has one enteringedge and one exiting edge, making it an ideal representation of the SUT for branch coverage. They
also mapped the result of data-flow analysis onto the ddgraph for testing requirements such as def-use
coverage. Once testing requirements are mapped to entities in the ddgraph, the test suite minimisation
problem can be reduced to the problem of finding the minimal spanning set.
Tallam and Gupta developed the greedy approach further by introducing the delayed greedy
approach, which is based on the Formal Concept Analysis of the relation between test cases and testing
requirements [18]. A potential weakness of the greedy approach is that the early selection made by thegreedy algorithm can eventually be rendered redundant by the test cases subsequently selected. For
example, consider the test suite and testing requirements depicted in Table I, taken from Tallam andGupta [18]. The greedy approach will select t1 first as it satisfies the maximum number of testing

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requirements, and then continues to select t2 , t3 and t4 . However, after the selection of t2 , t3 and t4 , t1
is rendered redundant. Tallam et al. tried to overcome this weakness by constructing a concept lattice,a hierarchical clustering based on the relation between test cases and testing requirements. Tallam etal. performed two types of reduction on the concept lattice. First, if a set of requirements coveredby ti is a superset of the set of requirements covered by tj , then tj is removed from the test suite.
Second, if a set of test cases that cover requirement ri is a subset of the set of test cases that coverrequirement rj , requirement ri is removed. The concept lattice is a natural representation that supports
the identification of these test cases. Finally, the greedy algorithm is applied to the transformed setof test cases and testing requirements. In the empirical evaluation, the test suites minimised by this‘delayed greedy’ approach were either the same size or smaller than those minimised by the classicalgreedy approach or by the HGS heuristic.
Jeffrey and Gupta extended the HGS heuristic so that certain test cases are selectively retained [19,20]. This ‘selective redundancy’ is obtained by introducing a secondary set of testing requirements.
When a test case is marked as redundant with respect to the first set of testing requirements, Jeffrey
and Gupta considered whether the test case is also redundant with respect to the second set of testing
requirements. If it is not, the test case is still selected, resulting in a certain level of redundancy withrespect to the first set of testing requirements. The empirical evaluation used branch coverage as thefirst set of testing requirements and all-uses coverage information obtained by data-flow analysis. The
results were compared to two versions of the HGS heuristic, based on branch coverage and def-use
coverage. The results showed that, while their technique produced larger test suites, the fault detection
capability was better preserved compared to single-criterion versions of the HGS heuristic.
Whereas the selective redundancy approach considers the secondary criterion only when a test caseis marked as being redundant by the first criterion, Black et al. considered a bi-criteria approach thattakes into account both testing criteria [21]. They combined the def-use coverage criterion with thepast fault detection history of each test case using a weighted-sum approach and used Integer Linear
Programming (ILP) optimisation to find subsets. The weighted-sum approach uses weighting factors
to combine multiple objectives. For example, given a weighting factor α and two objectives o1 and o2 ,the new and combined objective, o0 , is defined as follows:o0 = αo1 + (1 − α)o2
Consideration of a secondary objective using the weighted-sum approach has been used in otherminimisation approaches [22] and prioritisation approaches [23]. Hsu and Orso also considered the useof an ILP solver with multi-criteria test suite minimisation [22]. They extended the work of Black et
al. by comparing different heuristics for a multi-criteria ILP formulation: the weighted-sum approach,the prioritised optimisation and a hybrid approach. In prioritised optimisation, the human user assigns
a priority to each of the given criteria. After optimising for the first criterion, the result is added asa constraint, while optimising for the second criterion, and so on. However, one possible weakness
shared by these approaches is that they require additional input from the user of the technique in theforms of weighting coefficients or priority assignment, which might be biased, unavailable or costly toprovide.
Contrary to these approaches, Yoo and Harman treated the problem of time-aware prioritisation
as a multi-objective optimisation problem [24]. Instead of using a fitness function that combines
selection and prioritisation, they used a Pareto-efficient multi-objective evolutionary algorithm to

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simultaneously optimise for multiple objectives. You and Harman argued that the resulting Paretofrontier not only provides solutions but also allows the tester to observe trade-offs between objectives,
providing additional insights.
McMaster and Memon proposed a test suite minimisation technique based on call-stack coverage.
A test suite is represented by a set of unique maximum depth call stacks; its minimised test suite isa subset of the original test suite whose execution generates the same set of unique maximum depth
call stacks. Note that their approach is different from simply using function coverage for test suite
minimisation. Consider two test cases, t1 and t2 , respectively producing call stacks c1 =< f1 , f2 , f3 >and c2 =< f1 , f2 >. With respect to function coverage, t2 is rendered redundant by t1 . However,
McMaster and Memon regard c2 to be unique from c1 . For example, it may be that t2 detects a failurethat prevents the invocation of function f3 . Once the call-stack coverage information is collected, the
HGS heuristic can be applied. McMaster and Memon later applied the same approach to Graphical
User Interface (GUI) testing [25]. It was also implemented for object-oriented systems by Smith etal. [26].
While most test suite minimisation techniques are based on some kind of coverage criteria, there
do exist interesting exceptions. Harder et al. approached test suite minimisation using operational
abstraction [27]. An operational abstraction is a formal mathematical description of program behaviour.
While it is identical to formal specifications in form, an operational abstraction expresses dynamicallyobserved behaviour. Harder et al. use the widely studied Daikon dynamic invariant detector [28] toobtain operational abstractions. Daikon requires executions of test cases for the detection of possible
program invariants. Test suite minimisation is proposed as follows: if the removal of a test case does
not change the detected program invariant, it is rendered redundant. They compared the operational
abstraction approach to branch coverage based minimisation. While their approach resulted in larger
test suites, it also maintained higher fault detection capability. Moreover, Harder et al. also showedthat test suites minimised for coverage adequacy can often be improved by considering operational
abstraction as an additional minimisation criterion.
Leitner et al. propose a somewhat different version of the minimisation problem [29]. They startfrom the assumption that they already have a failing test case, which is too complex and too long forthe human tester to understand. Note that this is often the case with randomly generated test data;
the test case is often simply too complex for the human to establish the cause of failure. The goal ofminimisation is to produce a shorter version of the test case; the testing requirement is that the shorter
test case should still reproduce the failure. This minimisation problem is interesting because there isno uncertainty about the fault detection capability; it is given as a testing requirement. Leitner et al.
applied the widely studied delta-debugging technique [30] to reduce the size of the failing test case.
Schroeder and Korel proposed an approach of test suite minimisation for black-box software
testing [31]. They noted that the traditional approach of testing black-box software with combinatorial
test suites may result in redundancy, since certain inputs to the software may not affect the outcomeof the output being tested. They first identified, for each output variable, the set of input variables
that can affect the outcome. Then, for each output variable, an individual combinatorial test suite is
generated with respect to only those input variables that may affect the outcome. The overall test suite
is a union of all combinatorial test suites for individual output variables. This has a strong connection
to the concept of Interaction Testing, which is discussed in detail in Section 5.2.
Other work has focused on model-based test suite minimisation [32, 33]. Vaysburg et al. introduced
a minimisation technique for model based test suites that uses dependence analysis of Extended Finite

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State Machines (EFSMs) [32]. Each test case for the model is a sequence of transitions. Throughdependence analysis of the transition being tested, it is possible to identify the transitions that affectthe tested transition. In other words, testing a transition T can be thought of as testing a set ofother transitions that affect T . If a particular test case tests the same set of transitions as some othertest case, then it is considered to be redundant. Korel et al. extended this approach by combining
the technique with automatic identification of changes in the model [33]. The dependence analysis
based minimisation technique was applied to the set of test cases that were identified to execute themodified transitions. Chen et al. extended Korel’s model-based approach to incorporate more complex
representations of model changes [34].
A risk shared by most test suite minimisation techniques is that a discarded test case may detect
a fault. In some domains, however, test suite minimisation techniques can enjoy the certainty ofguaranteeing that discarding a test case will not reduce the fault detection capability. Anido et al.
investigated test suite minimisation for testing Finite State Machines (FSMs) in this context [35]. When
only some components of the SUT need testing, the system can be represented as a composition of two
FSMs: component and context. The context is assumed to be fault-free. Therefore, certain transitions of
the system that concern only the context can be identified to be redundant. Under the ‘testing in context’
assumption (i.e. the context is fault-free), it follows that it is possible to guarantee that a discarded test
case cannot detect faults.
Kaminski and Ammann investigated the use of a logic criterion to reduce test suites while
guaranteeing fault detection in testing predicates over Boolean vairables [36]. From the formal
description of fault classes, it is possible to derive a hierarchy of fault classes [37]. From the hierarchy,
it follows that the ability to detect a class of faults may guarantee the detection of another class.
Therefore, the size of a test suite can be reduced by executing only those test cases for the class offaults that subsume another class, whenever this is feasible.
3.2.

Impact on Fault Detection Capability

Although the techniques discussed so far reported reduced test suites, there has been a persistentconcern about the effect that the test suite minimisation has on the fault-detection capability of test
suites. Several empirical studies were conducted to investigate this effect [8, 38–40].
Wong, Horgan, London and Mathur studied ten common Unix programs using randomly generated
test suites; this empirical study is often referred to as the WHLM study [39]. To reduce the size of thetest suites, they used the ATAC testing tool developed by Horgan and London [13,15]. First, a large poolof test cases was created using a random test data generation method. From this pool, several test suites
with different total block coverage were generated. After generating test suites randomly, artificial
faults were seeded into the programs. These artificial faults were then categorised into 4 groups. Faults
in Quartile-I can be detected by [0 − 25)% of the test cases from the original test suite; the percentage
for Quartile-II, III, and IV is [25 − 50)%, [50 − 75)%, and [75 − 100]% respectively. Intuitively, faults
in Quartile-I are harder to detect than those in Quartile-IV. The effectiveness of the minimisation itself
was calculated as follows:(1 −number of test cases in the reduced test suite) ∗ 100%
number of test cases in the original test suite

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The impact of test suite minimisation was measured by calculating the reduction in fault detection
effectiveness as follows:(1 −number of faults detected by the reduced test suite) ∗ 100%
number of faults detected by the original test suite

By categorising the test suites (by different levels of block coverage) and test cases (by difficulty ofdetection), they arrived at the following observation. First, the reduction in size is greater in test suites
with a higher block coverage in most cases. This is natural considering that test suites with higher
block coverage will require more test cases in general. The average size reduction for test suites with
(50-55)%, (60-65)%,(70-75)%,(80-85)%, and (90-95)% block coverage was 1.19%, 4.46%, 7.78%,
17.44%, 44.23% respectively. Second, the fault detection effectiveness was decreased by test casereduction, but the overall decrease in fault detection effectiveness is not excessive and could be regarded
as worthwhile for the reduced cost. The average effectiveness reduction for test suites with (50-55)%,(60-65)%,(70-75)%,(80-85)%, and (90-95)% block coverage was 0%, 0.03%, 0.01%, 0.38%, 1.45%
respectively. Third, test suite minimisation did not decrease the fault detection effectiveness for faults
in Quartile-IV at all, meaning all faults in Quartile-IV had been detected by the reduced test suite.
The average decrease in fault detection effectiveness for Quartile-I, II, and III was 0.39%, 0.66%, and
0.098% respectively. The WHLM study concluded that, if the cost of testing is directly related to thenumber of test cases, then the use of the reduction technique is recommended.
Wong, Horgan, London and Pasquini followed up on the WHLM study by applying the ATAC tool
to test suites of another, bigger C program; this empirical study is often referred to as the WHLP
study [40]. The studied program, space, was an interpreter for the ADL(Array Description Language)
developed by the European Space Agency. In the WHLP study, test cases were generated, not randomly,
but from the operational profiles of space. That is, each test case in the test case pool was generated
so that it matches an example of real usage of space recorded in an operational profile. From the test
case pool, different types of test suites were generated. The first group of test suites was constructed byrandomly choosing a fixed number of test cases from the test case pool. The second group of test suites
was constructed by choosing test cases from the test case pool until a predetermined block coveragetarget was met. The faults in the program were not artificial, but real faults that were retrieved fromdevelopment logs.
The results of the WHLP study confirmed the findings of the WHLM study. As in the WHLM
study, test suites with low initial block coverage (50%, 55%, 60%, and 65%) showed no decrease infault detection effectiveness, after test suite minimisation. For both the fixed size test suites and fixed
coverage test suites, the application of the test case reduction technique did not affect the fault detection
effectiveness in any significant way; the average effectiveness reduction due to test suite minimisation
was less than 7.28%.
While both the WHLM and WHLP studies showed that the impact of test suite minimisation
on fault detection capability was insignificant, other empirical studies produced radically different
findings. Rothermel et al. also studied the impact of test suite minimisation on the fault detection
capability [38]. They applied the HGS heuristics to the Siemens suite [41], and later expanded this toinclude space [8]. The results from these empirical studies contradicted the previous findings of the
WHLM and WHMP studies.
For the study of the Siemens suite [38], Rothermel et al. constructed test suites from the test casepool provided by the Siemens suite so that the test suites include varying amounts of redundant test

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cases that do not contribute to the decision coverage of the test suite. The effectiveness and impact of
reduction was measured using the same metrics that were used in the WHLM study.
Rothermel et al. reported that the application of the test suite minimisation technique produced
significant savings in test suite size. The observed tendency in size reduction suggested a logarithmic
relation between the original test suite size and the reduction effectiveness. The results of logarithmic
regression confirmed this.
However, Rothermel et al. also reported that, due to the size reduction, the fault detection capabilities
of test suites were severely compromised. The reduction in fault detection effectiveness was over 50%
for more than half of 1,000 test suites considered, with some cases reaching 100%. Rothermel et al.
also reported that, unlike the size reduction effectiveness, the fault detection effectiveness did not show
any particular correlation with the original test suite size.
This initial empirical study was subsequently extended [8]. For the Siemens suite, the results ofthe HGS heuristic were compared to random reduction by measuring the fault detection effectivenessof randomly reduced test suites. Random reduction was performed by randomly selecting, from theoriginal test suite, the same number of test cases as in the reduced version of the test suite. The results
showed that random reduction produced larger decreases in fault detection effectiveness. To summarise
the results for the Siemens suite, the test suite minimisation technique produced savings in test suite
size, but at the cost of decreased fault detection effectiveness; however, the reduction heuristic showed
better fault detection effectiveness than the random reduction technique.
Rothermel et al. also expanded the previous empirical study by including the larger program,
space [8]. The reduction in size observed in the test suites of space confirmed the findings of theprevious empirical study of the Siemens suite; the size reduction effectiveness formed a logarithmic
trend, plotted against the original test suite size, similar to the programs in the Siemens suite. More
importantly, the reduction in fault detection effectiveness was less than those of the Siemens suite
programs. The average reduction in fault detection effectiveness of test suites reduced by the HGS
heuristic was 8.9%, while that of test suites reduced by random reduction was 18.1%.
Although the average reduction in fault detection effectiveness is not far from that reported for the
WHLP study in the case of space, those of the Siemens suite differed significantly from both the
WHLP study and the WHLM study, which reported that the application of the minimisation technique
did not have significant impact on fault detection effectiveness. Rothermel et al. [8] pointed out thefollowing differences between these empirical studies as candidates for the cause(s) of the contradictory
findings, which is paraphrased as follows:
1. Different subject programs: the programs in the Siemens suite are generally larger than thosestudied in both the WHLM and the WHLP study. Difference in program size and structure
certainly could have impact on the fault detection effectiveness.
2. Different types of test suites: the WHLM study used test suites that were not coverage-adequate
and much smaller compared to test suites used by Rothermel et al. The initial test pools usedin the WHLM study also did not necessarily contain any minimum number of test cases percovered item. These differences could have contributed to less redundancy in test suites, which
led to reduced likelihood that test suite minimisation will exclude fault-revealing test cases.
3. Different types of test cases: the test suites used in the WHLM study contained a few test casesthat detected all or most of the faults. When such strong test cases are present, reduced versionsof the test suites may well show little loss in fault-detection effectiveness.

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4. Different types of faults: the faults studied by Rothermel et al. were all Quartile-I faults according
to the definition of the WHLM study, whereas only 41% of the faults studied in the WHLM study
belonged to the Quartile-I group. By having more ‘easy-to-detect’ faults, the test suites used in
the WHLM study could have shown less reduction in fault-detection effectiveness after test suite
minimisation.

Considering the two contradicting empirical results, it is natural to conclude that the question ofevaluating the effectiveness of the test suite minimisation technique is very hard to answer in generaland for all testing scenarios. The answer depends on too many factors, such as the structure of the SUT,
the quality of test cases and test suites and the types of faults present. This proliferation of potential
contributory factors makes it very difficult to generalise any empirical result.
The empirical studies from WHLM, WHLP and Rothermel et al. all evaluated the effectiveness oftest suite minimisation in terms of two metrics: percentage size reduction and percentage fault detection
reduction. McMaster and Memon noticed that neither metric considers the actual role each testing
requirement plays on fault detection [42]. Given a set of test cases, T C, a set of known faults, KF anda set of testing requirements, CR, fault correlation for a testing requirement i ∈ CR to fault k ∈ KF
is defined as follows:|{j ∈ T C|j covers i} ∩ {j ∈ T C|j detects k}||{j ∈ T C|j covers i}|
The expected probability of finding a given fault k after test suite minimisation is defined as the
maximum fault correlation of all testing requirements with k. From this, the probability of detecting
all known faults in KF is the product of expected probability of finding all k ∈ KF . Since CR isdefined by the choice of minimisation criterion, e.g. branch coverage or call-stack coverage, comparing
the probability of detecting all known faults provides a systematic method of comparing different
minimisation criteria, without depending on a specific heuristic for minimisation. The empiricalevaluation of McMaster and Memon compared five different minimisation criteria for the minimisationof test suites for GUI-intensive applications: event coverage (i.e. each event is considered as a testing
requirement), event interaction coverage (i.e. each pair of events is considered as a testing requirement),
function coverage, statement coverage and call-stack coverage proposed in [43]. While call-stackcoverage achieved the highest average probability of detecting all known faults, McMaster and Memonalso found that different faults correlate more highly with different criteria. This analysis provides
valuable insights into the selection of minimisation criterion.
Yu et al. considered the effect of test suite minimisation on fault localisation [44]. They applied
various test suite minimisation techniques to a set of programs, and measured the impact of the size
reduction on the effectiveness of coverage-based fault localisation techniques. Yu et al. reported thathigher reduction in test suite size, typically achieved by statement coverage-based minimisation, tends
to have a negative impact on fault localisation, whereas minimisation techniques that maintain higher
levels of redundancy in test suites have negligible impact.

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4.

S.YOO AND M. HARMAN

Test Case Selection

Test case selection, or the regression test selection problem, is essentially similar to the test suite
minimisation problem; both problems are about choosing a subset of test cases from the test suite.
The key difference between these two approaches in the literature is whether the focus is upon thechanges in the SUT. Test suite minimisation is often based on metrics such as coverage measured
from a single version of the program under test. By contrast, in regression test selection, test cases are
selected because their execution is relevant to the changes between the previous and the current versionof the SUT.
To recall Definition 2, T 0 ideally should contain all the faults-revealing test cases in T , i.e., the test
cases that will reveal faults in P 0 . In order to define this formally, Rothermel and Harrold introduced
the concept of a modif ication − revealing test case [45]. A test case t is modification-revealing for
P and P 0 if and only if P (t) 6= P 0 (t). Given the following two assumptions, it is possible to identify
the fault-revealing test cases for P 0 by finding the modification-revealing test cases for P and P 0 .
• P -Correct-for-T Assumption : It is assumed that, for each test case t ∈ T , when P was tested
with t, P halted and produced the correct output.
• Obsolete-Test-Identification Assumption : It is assumed that there exists an effective procedurefor determining, for each test case t ∈ T , whether t is obsolete for P 0 .
From these assumptions, it is clear that every test case in T terminates and produces correct output
for P , and is also supposed to produce the same output for P 0 . Therefore, a modification-revealing test
case t must be also fault-revealing. Unfortunately, it is not possible to determine whether a test caset is fault-revealing against P 0 or not because it is undecidable whether P 0 will halt with t. Rothermel
considers a weaker criterion for the selection, which is to select all modification-traversing test cases
in T . A test case t is modification-traversing for P and P 0 if and only if :1. it executes new or modified code in P 0 , or
2. it formerly executed code that had been deleted in P 0
Rothermel also introduced the third assumption, which is the Controlled-Regression-Testing
assumption.
• Controlled-Regression-Testing Assumption : When P 0 is tested with t, all factors that might
influence the output of P 0 , except for the code in P 0 , are kept constant with respect to their states
when P was tested with t.
Given that the Controlled-Regression-Testing assumption holds, a non-obsolete test case t canthereby be modification-revealing only if it is also modification-traversing for P and P 0 . Now, if
the P -Correct-for-T assumption and the Obsolete-Test-Identification assumption hold along with theControlled-Regression-Testing assumption, then the following relation also holds between the subset
of fault-revealing test cases, Tf r , the subset of modification-revealing test cases, Tmr , the subset ofmodification-traversing test cases, Tmt , and the original test suite, T :Tf r = Tmr ⊆ Tmt ⊆ T

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Rothermel and Harrold admitted that the Controlled-Regression-Testing assumption may not be
always practical, since certain types of regression testing may make it impossible to control the testing
environment, e.g. testing of a system ported to different operating system [1]. Other factors like nondeterminism in programs and time dependencies are also difficult to control effectively. However,finding the subset of modification-traversing test cases may still be an useful approach in practice,because Tmt is the closest approximation to Tmr that can be achieved without executing all test cases.
In other words, by finding Tmt , it is possible to exclude those test cases that are guaranteed not to reveal
any fault in P 0 . The widely used term, safe regression test selection, is based on this concept [46]. A
safe regression test selection technique is not safe from all possible faults; however, it is safe in a sense
that, if there exists a modification-traversing test case in the test suite, it will definitely be selected.
Based on Rothermel’s formulation of the problem, it can be said that test case selection techniques
for regression testing focus on identifying the modification-traversing test cases in the given test suite.
The details of the selection procedure differ according to how a specific technique defines, seeks
and identifies modifications in the program under test. Various approaches have been proposed using
different techniques and criteria including Integer Programming [47, 48], data flow analysis [49–52],
symbolic execution [53], dynamic slicing [54], CFG graph-walking [46, 55–57], textual difference insource code, [58,59] SDG slicing [60], path analysis [61], modification detection [62], firewall [63–66],
CFG cluster identification [67] and design-based testing [68, 69]. The following subsections describe
these in more detail, highlighting their strengths and weaknesses.
4.1.

Integer Programming Approach

One of the earliest approaches to test case selection was presented by Fischer and Fischer et
al. who used Integer Programming (IP) to represent the selection problem for testing FORTRAN
programs [47, 48]. Lee and He implemented a similar technique [70]. Fischer first defined a program
segment as a single-entry, single exit block of code whose statements are executed sequentially. Their
selection algorithm relies on two matrices that describe the relation between program segments andtest cases, as well as between different program segments.
For a program with m segments and n test cases, the IP formulation is given as the problem offinding the decision vector,< x1 , . . . , xn >, that minimises the following objective function, Z :Z = c1 x1 + c2 x2 + . . . + cn xn
subject to :a11 x1 + a12 x2 + . . . + a1n xn ≥ b1
a21 x1 + a22 x2 + . . . + a2n xn ≥ b2···
am1 x1 + am2 x2 + . . . + amn xn ≥ bm
The decision vector, < x1 , . . . , xn >, represents the subset of selected test cases; xi is equal to 1 if
the ith test case is included; 0 otherwise. The coefficients, c1, . . . , cn , represent the cost of executing
each corresponding test case; Fischer et al. used the constant value of 1 for all coefficients, treating all

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test cases as being equally expensive. The test case dependency matrix, a11 , . . . , amn represents therelations between test cases and the program segments. The element aij is equal to 1 if the ith program
segment is executed by the test case j; 0 otherwise.
After deriving the series of inequalities, the set of bk values are determined by using a reachability
matrix that describes the program segments that are reachable from other segments. Using thisapproach, when the modified segments are known, it is possible to calculate all the segments thatare reachable from the modified segments, which thus need to be tested at least once. The integer
programming formulation is completed by assigning 1 to the b values for all the segments that need to
be tested. The inequality, ai1 x1 + . . . + ain xn ≥ bi , thus ensures that at least one included test casecovers the program element reachable from a change.
Hartmann and Robson implemented and extended a version of Fischer’s algorithm in order to apply
the technique to C [71–73]. They treat subroutines as segments, achieving subroutine coverage rather
than statement coverage.
One weakness in Fischer’s approach is its inability to deal with control-flow changes in P 0 . The test
case dependency matrix, a11 , . . . , amn , depends on the control-flow structure of the program under
test. If the control-flow structure changes, the test case dependency matrix can be updated only byexecuting all the test cases, which negates the point of applying the selection technique.
4.2.

Data-flow Analysis Approach

Several test case selection techniques have been proposed based on data-flow analysis [49–52]. Dataflow analysis based selection techniques seek to identify new, modified or deleted definition-use pairs
in P 0 , and select test cases that exercise these pairs.
Harrold and Soffa presented data-flow analysis as the testing criterion for an incremental approachto unit testing during the maintenance phase [50]. Taha, Thebaut, and Liu built upon this idea andpresented a test case selection framework based on an incremental data-flow analysis algorithm [52].
Harrold and Soffa developed both intra-procedural and inter-procedural selection techniques [51, 74].
Gupta et al. applied program slicing techniques to identify definition-use pairs that are affected by acode modification [49]. The use of slicing techniques enabled identification of definition-use pairs thatneed to be tested without performing a complete dataflow analysis, which is often very costly. Wong etal. combined a data-flow selection approach with a coverage-based minimisation and prioritisation tofurther reduce the effort [75].
One weakness shared by all data-flow analysis-based test case selection techniques is the fact thatthey are unable to detect modifications that are unrelated to data-flow change. For example, if P 0
contains new procedure calls without any parameter, or modified output statements that contain novariable uses, data-flow techniques may not select test cases that execute these.
Fisher II et al. applied the data-flow based regression test selection approach for test re-use in
spreadsheet programs [76]. Fisher II et al. proposed an approach called What-You-See-Is-WhatYou-Test (WYSIWYT) to provide incremental, responsive and visual feedback about the testednessof cells in spreadsheets. The WYSIWYT framework collects and updates data-flow information
incrementally as the user of the spreadsheet makes modifications to cells, using Cell Relation Graph(CRG). Interestingly, the data-flow analysis approach to re-test spreadsheets is largely free fromthe difficulties associated with data-flow testing of procedural code, because spreadsheet programs

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lack tricky semantic features such as aliasing and unstructured control flow. This makes spreadsheet
programs better suited to a data-flow analysis approach.
4.3.

Symbolic Execution Approach

Yau and Kishmoto presented a test case selection technique based on symbolic execution of the
SUT [53]. In symbolic execution of a program, the variables’ values are treated as symbols, rather
than concrete values [77]. Yau and Kishimoto’s approach can be thought of as an application ofsymbolic execution and input partitioning to the test case selection problem. First, the techniquestatically analyses the code and specifications to determine the input partitions. Next, it produces test
cases so that each input partition can be executed at least once. Given information on where the code
has been modified, the technique then identifies the edges in the control flow graph that lead to themodified code. While symbolically executing all test cases, the technique determines test cases thattraverse edges that do not reach any modification. The technique then selects all test cases that reach
new or modified code. For the symbolic test cases that reach modifications, the technique completes
the execution; the real test cases that match these symbolic test cases should be retested.
While it is theoretically powerful, the most important drawback of the symbolic execution approachis the algorithmic complexity of the symbolic execution. Yau and Kishmoto acknowledge that symbolic
execution can be very expensive. Pointer arithmetic can also present challenging problems for symbolic
execution based approaches.
4.4.

Dynamic Slicing Based Approach

Agrawal et al. introduced a family of test case selection techniques based on different program slicing
techniques [54]. An execution slice of a program with respect to a test case is what is usually referred
to as an execution trace; it is the set of statements executed by the given test case. A dynamic slice of aprogram with respect to a test case is the set of statements in the execution slice that have an influenceon an output statement. Since an execution slice may contain statements that do not affect the program
output, a dynamic slice is a subset of an execution slice. For example, consider the program shown in
Figure 1. It contains two faults in line S3 and S11 respectively. The execution slice of the programwith respect to test case T3 in Table II is shown in column ES of Figure 1. The dynamic slice of the
program with respect to test case T1 in Table II is shown in column DS of Figure 1.
In order to make the selection more precise, Agrawal et al. proposed two additional slicing criteria:a relevant slice and an approximate relevant slice. A relevant slice of a program with respect to a test
case is the dynamic slice with respect to the same test case together with all the predicate statements inthe program that, if evaluated differently, could have caused the program to produce a different output.
An approximated relevant slice is a more conservative approach to include predicates that could havecaused a different output; it is the dynamic slice with all the predicate statements in the execution slice.
By including all the predicates in the execution slice, an approximated relevant slice caters for theindirect references via pointers. For example, consider the correction of S3 in the program shown in
Figure 1. The dynamic slice of T4 does not include S3 because the class value of T4 is not affectedby any of the lines between S3 and S8. However, the relevant slice of T4 , shown in column DS of
Figure 1, does include S3 because it could have affected the output when evaluated differently.

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SlicesES DS RS
x x x
x
x
x
x
x
x
x
x
x

x
x
x
x

x
x

x
x
x
x
x

S1: read(a,b,c);
S2: class := scalene;
S3: if a = b or b = aS4:class := isosceles;
S5: if a * a = b * b + c * cS6:class := rightS7: if a = b and b = c
S8:class := equilateral
S9: case class of:
S10:
right
: area = b * c / 2;
S11:equilateral : area = a * 2 * sqrt(3) / 4;S12:otherwise: s := (a + b + c) / 2;
S13:
area := sqrt(s * (s-a) * (s-b) * (s-c));
S14: end;
S15: write(class, area);

Figure 1. Example triangle classification program taken from Agrawal et al. [54]. Note that it is assumed that the
input vector is sorted in descending order. It contains two faults. In S3, b = a should be b = c. In S11, a *2 should be a * a. The column ES represents the statements that belong to the execution slice with respect totest case T3 in table II. Similarly, column DS represents the dynamic slice with respect to test case T1 in Table II
and column RS the relevant slice with respect to test case T4 in Table II.

Testcase

T1
T2
T3
T4
T5
T6

Input

Output

a

b

c

class

area

2
4
5
6
3
4

2
4
4
5
3
3

2
3
3
4
3
3

equilateralisosceles
rightscaleneequilateralscalene

1.73
5.56
6.00
9.92
2.60
4.47

Table II. Test cases used with the program shown in Figure 1, taken from Agrawal et al. [54].
Note that T5 detects the fault in S11, because the value for area should be 3.90. Similarly, T6
detects the fault in S3, because the value for class should be isosceles.

The test suite and the previous version of the program under test are preprocessed using these slicing
criteria; each test case is connected to a slice, sl, constructed by one of the four slicing criteria. Afterthe program is modified, test cases for which sl contains the modified statement should be executed
again. For example, assume that the fault in S11, detected by T5 , is corrected. The program should
be retested with T5 . However, T3 need not be executed because the execution slice of T3 , shown incolumn ES of Figure 1, does not contain S11. Similarly, assume that the fault in S3, detected by T6 ,is corrected. The program should be retested with T6 . The execution slice technique selects all six

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test cases, T1 to T6 , after the correction of the fault in S3 because the execution slices of all six test
cases include S3. However, it is clear that T1 and T3 are not affected by the correction of S3; theirclass values are overwritten after the execution of S3. The dynamic slicing technique overcomes
this weakness. The dynamic slice of T1 is shown in column DS of Figure 1. Since S3 does not affectthe output of T1 , it is not included in the dynamic slice. Therefore, the modification of S3 does notnecessitate the execution of T1 .
Agrawal et al. first built their technique on cases in which modifications are restricted to thosethat do not alter the control flow graph of the program under test. As long as the control flow graphof the program remains the same, their technique is safe and can be regarded as an improvement
over Fischer’s integer programming approach. Slicing removes the need to formulate the linear
programming problem, reducing the effort required from the tester. Agrawal et al. later relaxed theassumption about static control flow graphs in order to cater for modifications in the control flow
graphs of the SUT. If a statement s is added to P , now the slice sl contains all the statements in P
that uses the variables defined in s. Similarly, if a predicate p is added to P , the slice sl contains all
the statements in P that are control-dependent on p. This does cater for the changes in the control
flow graph to some degree, but it is not complete. For example, if the added statement is a simple
output statement that does not define or use any variable, then this statement can still be modificationrevealing. However, since the new statement does not contain any variable, its addition will not affectany of the existing slices, resulting in an empty selection.
4.5.

Graph-Walk Approach

Rothermel and Harrold presented regression test case selection techniques based on graph walkingof Control Dependence Graphs (CDGs), Program Dependence Graphs (PDGs), System Dependence
Graphs (SDGs) and Control Flow Graphs (CFGs) [46, 56, 57, 78]. The CDG is similar to PDG but
lacks data dependency relations. By performing a depth-first traversal of the CDGs of both P and
P 0 , it is possible to identify points in a program through which the execution trace reaches themodifications [46]. If a node in the CDG of P is not lexically equivalent to the corresponding node in
the CDG of P 0 , the algorithm selects all the test cases that execute the control-dependence predecessorsof the mismatching node. The CDG based selection technique does not cater for inter-procedural
regression test case selection; Rothermel and Harrold recommend application of the technique at theindividual procedural level.
Rothermel and Harrold later extended the graph walking approach to use PDGs for intra-procedural
selection, and SDGs for inter-procedural selection [56]. A weakness of the CDG based technique isthat, due to the lack of data dependence, the technique will select test cases that execute modified
definitions but not the actual uses of a variable. If the modified definition of a variable is never used, itcannot contribute to any different output, and therefore its inclusion is not necessary for safe regression
testing. PDGs contain data dependence for a single procedure; SDGs extend this to a complete programwith multiple procedures. By using these graphs, Rothermel and Harrold’s algorithm is able to check
whether a modified definition of a variable is actually used later.
Rothermel and Harrold later presented the graph walking approach based on CFGs [57]. The CFGbased technique essentially follows the approach introduced for the CDG-based technique, but on
CFGs rather than on CDGs. Since CFG is a much simpler representation of the structure of a program,the CFG-based technique may be more efficient. However, the CFG lacks data dependence information,

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so the CFG based technique may select test cases that are not capable of producing different outputs
from the original programs, as explained above. The technique has been evaluated against various
combinations of subject programs and test suites [79]. Ball improved the precision of the graph walkapproach with respect to branch coverage [80].
Rothermel et al. extended the CFG-based graph walk approach for object-oriented software using theInterprocedural Control Flow Graph (ICFG) [81]. The ICFG connects methods using call and returnedges. Harrold et al. adopted a similar approach for test case selection for Java software, using theJava Interclass Graph as representation (JIG) [82]. Xu and Rountev later extended this technique toconsider AspectJ programs by incorporating the interactions between methods and advices at certain
join points into the CFG [83]. Zhao et al. also considered a graph representation of AspectJ programs
to apply a graph walk approach for RTS [84]. Beydeda and Gruhn extended the graph walk approachby adding black-box data-flow information to the Class Control Flow Graph (CCFG) to test objectoriented software [85].
Orso et al. considered using different types of graph representation of the system to improve
the scalability of graph-walk approach [86]. Their approach initially relies on a high-level graphrepresentation of SUT to identify the parts of the system to be further analysed. Subsequently, thetechnique uses more detailed graph representation to perform more precise selection.
One strength of the graph walk approach is its generic applicability. For example, it has beensuccessfully used in black-box testing of re-usable classes [87]. Martins and Vieira captured the
behaviours of a re-usable class by constructing a directed graph called the Behavioural Control
Flow Graph (BCFG) from the Activity Diagram (AD) of the class. The BCFG is a directed graph,
G = (V, E, s, x), with vertices V , edges E, a unique entry vertex s and an exit vertex x. Each vertex
contains a label that specifies the signature of a method; each edge is also labelled according to thecorresponding guards in AD. A path in G from s to x represents a possible life history of an object.
By mapping changes made to the object to its BCFG and applying the graph walking algorithm, it
is possible to select test cases based on the behavioural difference between two versions of the same
object. This approach requires traceability between the behavioural model and the actual test cases,
because test cases are selected, not based on their structural coverage, but based on their behavioural
coverage measured on BCFG. Activity diagrams have also been directly used for RTS by Chen etal. [88].
Orso et al. used a variation of the graph walk approach to consider an RTS technique based onmeta-data and specifications obtained from software components [89,90]. They presented two different
techniques based on meta-data: code-based RTS using component meta-data and specification-based
RTS using component meta-data. For code-based RTS, it was assumed that each software component
was capable of providing structural coverage information, which was fed into the graph walkalgorithm. For specification-based RTS, the component specification was represented in UML statechart diagrams, which were used by the graph walk algorithm.
The graph walk algorithm has also been applied to test web services, despite the challenges that arise
from the distributed nature of web services [91–95]. Several different approaches have been introduced
to overcome these challenges. Lin et al. adopted the JIG-based approach after transforming the webservices to a single-JVM local application [91]. Ruth et al. collected a coarse-grained CFG fromdevelopers of each web service that forms a part of the entire application [92,93,95]. Finally, Tarhini etal. utilised Timed Labeled Transition System (TLTS), which is a coarse-grained representation of web
services that resembles a labelled state machine [94].

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4.6.

21

Textual Difference Approach

Volkolos and Frankl proposed a selection technique based on the textual difference between the sourcecode of two versions of SUT [58, 59]. They identified modified parts of SUT by applying the diff
Unix tool to the source code of different versions. The source code was pre-processed into canonicalforms to remove the impact of cosmetic differences. Although their technique operates on a different
representation of SUT, its behaviour is essentially very similar to that of the CFG based graph-walkapproach.
4.7.

SDG Slicing Approach

Bates and Horwitz proposed test case selection techniques based on program slices from Program
Dependency Graphs (PDGs) [60]. Bates and Horwitz approach the regression test selection problem in
two stages. First, all the test cases that can be reused for P 0 need to be identified. Bates and Horwitz
introduce the definition of an equivalent execution pattern. If statements s and s0 belong to P and P 0
respectively, s and s0 have equivalent execution patterns if and only if all of the following hold:1. For any input file on which both P and P 0 terminate normally, s and s0 are exercised the same
number of times.
2. For any input file on which P terminates normally but P 0 does not, s0 is exercised at most as
many times as s is exercised.
3. For any input file on which P 0 terminates normally but P does not, s is exercised at most as
many times as s0 is exercised.
Using program slicing, Bates and Horwitz categorize statements into execution classes. Statement s
from P and s0 from P 0 belong to the same execution class if and only if any test that exercises s will
also exercise s0 .
Now, a statement s0 in P 0 is affected by the modification if and only if one of the following holds:
1. There is no corresponding statement s in P .
2. The behaviour of s0 is not equivalent to the corresponding statement s in P .
Equivalent behaviour is determined by PDG slice isomorphism; if the PDG slices of two statements
are isomorphic, then those statements share an equivalent behaviour. For each affected statement in P 0 ,reusable test cases are selected based on the information retrieved from the identification stage.
While Bates and Horwitz’s technique selects test cases for modified or newly added statements in
P 0 , it does not select tests that exercise statements that are deleted from P , and therefore is not safe.
Binkley [96, 97] presented a technique based on System Dependence Graph (SDG) slicing, which
extends Bates and Horwitz’s intra-procedural selection technique to inter-procedural test case selection.
Binkley introduced the concept of common execution patterns, which corresponds to the equivalent
execution patterns of Bates and Horwitz, to capture the multiple invocation of a procedure.
4.8.

Path Analysis

Benedusi et al. applied path analysis for test case selection [61]. They construct exemplar paths from P
and P 0 expressed in an algebraic expression. By comparing two sets of exemplar paths, they classified

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paths in P 0 as new, modified, cancelled, or unmodified. Test cases and the paths they execute in P are
known; therefore, they selected all the test cases that will traverse modified paths in P 0 .
One potential weakness of the path analysis approach of Benedusi et al. lies not in path analysis
itself, but in the potentially over-specific definition of ‘modification’ used in the post-analysis selection
phase. No test cases are selected for the paths that are classified to be new or cancelled. However, newor cancelled paths denote modifications that represent differences between P and P 0 ; test cases that
execute new or cancelled paths in P 0 may be modification-revealing. As presented, therefore, the pathanalysis approach is not safe.

4.9.

Modification-based Technique

Chen et al. introduced a testing framework called TestTube, which utilises a modification-based
technique to select test cases [62]. TestTube partitions the SUT into program entities, and monitors
the execution of test cases to establish connections between test cases and the program entities thatthey execute. TestTube also partitions P 0 into program entities, and identifies program entities thatare modified from P . All the test cases that execute the modified program entities in P should be
re-executed.
TestTube can be thought of as an extended version of the graph walk approach. Both techniques
identify modifications by examining the program source code, and select test cases that will executethe modified parts. TestTube extends the CDG-based graph walk technique by introducing program
entities that include both functions and entities that are not functions, i.e. variables, data types, and preprocessor macros. Any test case that executes modified functions will be selected. Therefore, TestTube
is a safe test case selection technique.
One weakness of TestTube is pointer handling. By including variable and data types as program
entities, TestTube requires that all value creations and manipulations in a program can be inferred fromsource code analysis. This is only valid for languages without pointer arithmetic and type coercion.
As a result, TestTube makes assumptions; for example, it assumes that all pointer arithmetics are wellbounded. If these assumptions do not hold then safety cannot be guaranteed.

4.10.

Firewall Approach

Leung and White introduced and later implemented what they called a firewall technique for regression
testing of system integration [63–66]. The main concept is to draw a firewall around the modules ofthe system that need to be retested. They categorise modules into the following categories:• No Change : module A has not been modified, N oCh(A).
• Only Code Change : module A has the same specification but its code has been modified,
CodeCh(A).
• Spec Change : module A has modified specifications, SpecCh(A).
If a module A calls a module B, there exist 9 possible pairings between the states of A and B. The
integration between A and B can be ignored for regression testing if N oCh(A) ∧ N oCh(B), leaving 8

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pairings. If both A and B are modified either in code or in specifications, the integration tests between
A and B should be executed again as well as the unit tests of A and B; this accounts for 4 of theremaining 8 pairings. The other 4 pairings are cases in which an unchanged module calls a changed
module, or vice versa; these pairs form the boundary for integration testing, i.e. the so-called firewall.
By considering modules as the atomic entities, Leung and White maintained a very conservative
approach to test case selection. If a module has been modified, any test case that tests the integrationof the modified module should be selected. Therefore, all modification-traversing test cases will beselected. However, their technique may also select other test cases that execute the modified module,
but are not modification-traversing in any way. Leung and White also noted that, in practice, the test
suite for system integration is often not very reliable. The low reliability means that it is more likelythat there may still exist a fault-revealing test case that does not belong to the test suite, and therefore
cannot be selected. Note that it is always a risk that a fault-revealing test case exists outside the given
test suite in any type of testing, not only in integration testing. What Leung and White pointed out
was that such a risk can be higher in system integration testing due to the generally low quality of test
suites.
The Firewall approach has been applied to Object-Oriented programs [98–100] and GUIs [101].
Firewall approach has also been successfully applied to RTS for black-box Commercial Off-the-Shelf(COTS) components. Zheng et al. applied the firewall technique of Leung and White based on theinformation extracted from the deployed binary code [102–105]. Skoglund and Runeson applied thefirewall approach to a large-scale banking system [106].
4.11.

Cluster Identification

Laski and Szemer presented a test case selection technique based on analysis of the CFG of the program
under test [67]. Their technique identifies single-entry, single-exit subgraphs of CFG called clusters.
Given a program P and its modified version P 0 ,• each cluster in P encapsulates some modifications to P ,• there is a unique cluster in P 0 that corresponds to the cluster in P , and• when clusters in each graph are replaced by single nodes, there is a one-to-one correspondence
between nodes in both graphs.
The CFGs of the original program and the modified program are reduced using a set of operatorssuch as node collapse and node removal. During the process, if the counterpart of a node from the CFGof the original program cannot be found in the CFG of the modified program, this node is labelled as‘MOD’, indicating a modification at the node. Eventually, all the modifications will be enclosed in one
or more MOD cluster nodes. As with other test case selection techniques, their technique requires thatthe tester records the execution history of each test case in the test suite. Once clustering is completed,test case selection is performed by selecting all the test cases for which the corresponding execution
path enters any of the MOD clusters.
The strength of the cluster identification technique is that it guarantees to select all modificationtraversing test cases regardless of the type of the modification, i.e. addition or deletion of statements
and control structures. However, since the clusters can encapsulate much larger areas of the SUT than
the scope of actual modification, the technique may also select test cases that are not modificationtraversing. In this sense the approach sacrifices precision in order to achieve safety.

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4.12.

Design-based Approach

Briand et al. presented a black-box, design level regression test selection approach for UML-based
designs [68, 69]. Assuming that there is traceability between the design and regression test cases,
it is possible to perform regression test selection of code-level test cases from the impact analysisof UML design models. Briand et al. formalised possible changes in UML models, and classified
the relevant test cases into the categories defined by Leung and White [10]: obsolete, retestable andreusable. They implemented an automated impact analysis tool for UML and empirically evaluated itusing both student projects and industrial case studies.
The results showed that the changes made to a model can have a widely variable impact on
the resulting system, which, in turn, yields varying degrees of reduction of effort in terms of thenumber of selected test cases. However, Briand et al. noted that the automated impact analysis
itself can be valuable, especially for very large systems, such as the cruise control and monitoring
system they studied. The UML use-cases of the model of the system had 323,614 corresponding
test cases. UML-based models also have been considered by Dent et al. [107], Pilskalns et al. [108]and Farooq et al. [109] for regression test selection; Le Traon et al. [110] and Wu and Offutt [111]considered the use of UML models in the wider context of regression testing in general. Muccini et al.
considered the RTS problem at the software architecture level, although they did not use UML for the
representation [112, 113].

5. Test Case Prioritisation
Test case prioritisation seeks to find the ideal ordering of test cases for testing, so that the tester obtains
maximum benefit, even if the testing is prematurely halted at some arbitrary point. The approach was
first mentioned by Wong et al. [39]. However, in that work it was only applied to test cases that werealready selected by a test case selection technique. Harrold and Rothermel [114, 115] proposed andevaluated the approach in a more general context.
For example, consider the test suite described in Table III. Note that the example depicts an ideal
situation in which fault detection information is known. The goal of prioritisation is to maximise
early fault detection. It is obvious that the ordering A-B-C-D-E is inferior to B-A-C-D-E. In fact, any
ordering that starts with the execution of C-E is superior to those that do not, because the subsequence
C-E detects faults as early as possible; should testing be stopped prematurely, this ensures that the
maximum possible fault coverage will have been achieved.
Note that the problem definition concerns neither versions of the program under test, nor exact
knowledge of modifications. Ideally, the test cases should be executed in the order that maximises
early fault detection. However, fault detection information is typically not known until the testing isfinished. In order to overcome the difficulty of knowing which tests reveal faults, test case prioritisation
techniques depend on surrogates, hoping that early maximisation of a certain chosen surrogate property
will result in maximisation of earlier fault detection. In a controlled regression testing environment, theresult of prioritisation can be evaluated by executing test cases according to the fault detection rate.

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Test Case

Fault revealed by test case
1

A
B
C
D
E

25

x
x
x

2

x

3

x

4

x

5

6

7

x
x
x
x

x
x

x
x

8

9

10

x

x

x

Table III. Example test suite with fault detection information, taken from Elbaum et al [116]. It
is clearly beneficial to execute test case C first, followed by E.

5.1.

Coverage-based Prioritisation

Structural coverage is a metric that is often used as the prioritisation criterion [115–121]. The intuition
behind the idea is that early maximisation of structural coverage will also increase the chance ofearly maximisation of fault detection. Therefore, while the goal of test case prioritisation remainsthat of achieving a higher fault detection rate, prioritisation techniques actually aim to maximise early
coverage.
Rothermel et al. reported empirical studies of several prioritisation techniques [115, 121]. They
applied the same algorithm with different fault detection rate surrogates. The considered surrogates
were: branch-total, branch-additional, statement-total, statement-additional, Fault Exposing Potential(FEP)-total, and FEP-additional.
The branch-total approach prioritises test cases according to the number of branches covered byindividual test cases, while branch-additional prioritises test cases according to the additional numberof branches covered by individual test cases. The statement-total and statement-additional approaches
apply the same idea to program statements, rather than branches. Algorithmically, ‘total’ approaches
are essentially instances of greedy algorithms whereas ‘additional’ approaches are essentially instancesof additional greedy algorithms.
The FEP of a test case is measured using program mutation. Program mutation introduces a simple
syntactic modification to the program source, producing a mutant version of the program [122]. Thismutant is said to be killed by a test case if the test case reveals the difference between the original
program and the mutant. Given a set of mutants, the mutation score of a test case is the ratio of mutants
that are killed by the test case to the total kill-able mutants. The FEP-total approach prioritises test cases
according to the mutation score of individual test cases, while the FEP-additional approach prioritises
test cases according to the additional increase in mutation score provided by individual test cases. Notethat FEP criterion can be constructed to be at least as strong as structural coverage; to kill a mutant,a test case not only needs to achieve the coverage of the location of mutation but also to execute themutated part with a set of test inputs that can kill the mutant. In other words, coverage is necessary but
not sufficient to kill the mutants.
It is important to note that all the ‘additional’ approaches may reach 100% realisation of the utilised
surrogate before every test case is prioritised. For example, achieving 100% branch coverage may not

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require all the test cases in the test suite, in which case none of the remaining test cases can increase
the branch coverage. Rothermel et al. reverted to the ‘total’ approach once such a condition is met.
The results were evaluated using the Average Percentage of Fault Detection (APFD) metric. Higher
APFD values denote faster fault detection rates. When plotting the percentage of detected faults against
the number of executed test cases, APFD can be calculated as the area below the plotted line. Moreformally, let T be the test suite containing n test cases and let F be the set of m faults revealed by T .
For ordering T 0 , let T Fi be the order of the first test case that reveals the ith fault. The APFD value forT 0 is calculated as following [123]:
AP F D = 1 −1
T F1 + . . . + T Fm+
nm
2nNote that, while APFD is commonly used to evaluate test case prioritisation techniques, it is not theaim of test case prioritisation techniques to maximise APFD. Maximisation of APFD would be possible
only when every fault that can be detected by the given test suite is already known. This would imply
that all test cases have been already executed, which would nullify the need to prioritise. APFD iscomputed after the prioritisation only to evaluate the performance of the prioritisation technique.
Rothermel et al. compared the proposed prioritisation techniques to random prioritisation, optimalprioritisation, and no prioritisation, using the Siemens suite programs. Optimal prioritisation is possible
because the experiment was performed in a controlled environment, i.e. the faults were already known.
The results show that all the proposed techniques produce higher APFD values than random or no
prioritisation. The surrogate with the highest APFD value differed between programs, suggestingthat there is no single best surrogate. However, on average across the programs, the FEP-additional
approach performed most effectively, producing APFD value of 74.5% compared to the 88.5% of the
optimal approach. It should still be noted that these results are dependent on many factors, including
the types of faults used for evaluation and types of mutation used for FEP, limiting the scope forgeneralisation.
Elbaum et al. extended the empirical study of Rothermel et al. by including more programs
and prioritisation surrogates [116]. Among the newly introduced prioritisation surrogates, functioncoverage and function-level FEP enabled Elbaum et al. to study the effects of granularity onprioritisation. Function-coverage of a test case is calculated by counting the number of functions thatthe test case executes. Function-level FEP is calculated, for each function f and each test case t, bysumming the ratio of mutants in f killed by t. Elbaum et al. hypothesised that approaches with coarser
granularity would produce lower APFD values, which was confirmed statistically.
Jones and Harrold applied the greedy-based prioritisation approach to Modified Condition/Decision
Coverage (MC/DC) criterion [124]. MC/DC is a ‘stricter form’ of branch coverage; it requires
execution coverage at condition level. A condition is a Boolean expression that cannot be factored into
simpler Boolean expressions. By checking each condition in decision predicates, MC/DC examines
whether each condition independently affects the outcome of the decision [125]. They presented anempirical study that contained only an execution time analysis of the prioritisation technique and not
an evaluation based on fault detection rate.
Srivastava and Thiagarajan combined the greedy-based prioritisation approach with regression test
selection [126]. They first identified the modified code blocks in the new version of the SUT bycomparing its binary code to that of the previous version. Once the modified blocks are identified, test

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case prioritisation is performed using greedy-based prioritisation, but only with respect to the coverageof modified blocks.
Do and Rothermel applied coverage-based prioritisation techniques to the JUnit testing environment,
a popular unit testing framework [127]. The results showed that prioritised execution of JUnit test cases
improved the fault detection rate. One interesting finding was that the random prioritisation sometimes
resulted in an APFD value higher than the untreated ordering, i.e. the order of creation. When executedin the order of creation, newer unit tests are executed later. However, it is the newer unit tests that have ahigher chance of detecting faults. The empirical results showed that random prioritisation could exploit
this weakness of untreated ordering in some cases.
Li et al. applied various meta-heuristics for test case prioritisation [128]. They compared randomprioritisation, a hill climbing algorithm, a genetic algorithm, a greedy algorithm, the additional
greedy algorithm, and a two-optimal greedy algorithm. The greedy algorithm corresponds to the total
approaches outlined above, whereas the additional greedy algorithm corresponds to the additional
approaches outlined above. The two-optimal greedy is similar to the greedy algorithm except thatit considers two candidates at the same time rather than a single candidate for the next order. They
considered the Siemens suite programs and the program space, and evaluated each technique basedon APBC (Average Percentage of Block Coverage) instead of APFD. The results showed that theadditional greedy algorithm is the most efficient in general.
5.2.

Interaction Testing

Interaction testing is required when the SUT involves multiple combinations of different components.
A common example would be configuration testing, which is required to ensure that the SUT executes
correctly on different combinations of environment, such as different operating systems or hardware
options. Each component that can be changed is called a factor; the number of choices for each factor
is called the level of the corresponding factor. As the number of factors and levels of each factor
increase, exhaustive testing of all possible combinations of factors becomes infeasible as it requires an
exponentially large test suite.
Instead of testing exhaustively, pair-wise interaction testing requires only that every individual
pair of interactions between different factors are included at least once in the testing process. The
reduction grows larger as more factors and levels are involved. More formally, the problem of obtaining
interaction testing combinations can be expressed as the problem of obtaining a covering array,
CA(N ; t, k, v), which is an array with N rows and k columns; v is the number of levels associatedwith each factor, and t is the strength of the interaction coverage (2 in the case of pair-wise interaction
testing).
One important research direction in interaction testing is how to efficiently generate an interaction
test suite with high interaction coverage. This shares the same basic principles of test case prioritisation.
For example, the greedy approach aims to find, one by one, the ‘next’ test case that will mostincrease the k-way interaction coverage [129, 130], which resembles the greedy approach to test caseprioritisation. However, the similarities are not just limited to the generation of interaction test suites.
Bryce and Colbourn assume that testers may value certain interactions higher than others [131, 132].
For example, an operating system with a larger user base may be more important than one witha smaller user base. After weighting each level value for each factor, they calculate the combined
benefit of a given test by adding the weights of each level value selected for the test. They present a

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Deterministic Density Algorithm (DDA) that prioritises interaction tests according to their combined
benefit. Qu et al. compared different weighting schemes used for prioritising covering arrays [133,134].
Bryce and Memon also applied the principles of interaction coverage to the test case prioritisationof Event-Driven Software (EDS) [135]. EDS takes sequences of events as input, changes state andoutputs new event sequences. A common example would be GUI-based programs. Bryce and Memoninterpreted t-way interaction coverage as sequences that contain different combinations of events over t
unique GUI windows. Interaction coverage based prioritisation of test suites was compared to different
prioritisation techniques such as unique event coverage (the aim is to cover as many unique events as
possible, as early as possible), longest to shortest (execute the test case with the longest event sequence
first) and shortest to longest (execute the test case with the shortest event sequence first). The empiricalevaluation showed that interaction coverage based testing of EDS can be more efficient than the othertechniques, provided that the original test suite contains higher interaction coverage. Note that Bryce
and Memon did not try to generate additional test cases to improve interaction coverage; they only
considered permutations of existing test cases.
5.3.

Prioritisation Approaches Based on Other Criteria

While the majority of existing prioritisation literature concerns structural coverage in some form or
another, there are prioritisation techniques based on other criteria [136–139].
Distribution-based Approach Leon and Podgurski introduced distribution-based filtering andprioritisation [136]. Distribution-based techniques minimise and prioritise test cases based on the
distribution of the profiles of test cases in the multi-dimensional profile space. Test case profiles areproduced by the dissimilarity metric, a function that produces a real number representing the degreeof dissimilarity between two input profiles. Using this metric, test cases can be clustered according totheir similarities. The clustering can reveal some interesting information. For example:• Clusters of similar profiles may indicate a group of redundant test cases• Isolated clusters may contain test cases inducing unusual conditions that are perhaps more likely
to cause failures• Low density regions of the profile space may indicate uncommon usage behaviours
The first point is related to reduction of effort; if test cases in a cluster are indeed very similar, it may
be sufficient to execute only one of them. The second and third points are related to fault-proneness.
Certain unusual conditions and uncommon behaviours may tend to be harder to reproduce than morecommon conditions and behaviours. Therefore, the corresponding parts of the program are likely tobe tested less than other, more frequently used parts of the program. Assigning a high priority to test
cases that execute these unusual behaviours may increase the chance of early fault detection. A good
example might be exception handling code.
Leon and Podgurski developed new prioritisation techniques that combine coverage-based prioritisation with distribution-based prioritisation. This hybrid approach is based on the observation thatbasic coverage maximisation performs reasonably well compared to repeated coverage maximisation.
Repeated coverage maximisation refers to the prioritisation technique of Elbaum et al. [116], which,after realising 100% coverage, repeatedly prioritises test cases starting from 0% coverage again. In

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contrast, basic coverage maximisation stops prioritising when 100% coverage is achieved. Leon andPodgurski observed that the fault detection rate of repeated coverage maximisation is not as high as thatof basic coverage maximisation. This motivated them to consider a hybrid approach that first prioritises
test cases based on coverage, then switches to distribution-based prioritisation once the basic coverage
maximisation is achieved. They considered two different distribution-based techniques. The one-percluster approach samples one test case from each cluster, and prioritises them according to the orderof cluster creation during the clustering. The failure-pursuit approach behaves similarly, but it adds the
k closest neighbours of any test case that finds a fault. The results showed that the distribution-basedprioritisation techniques could outperform repeated coverage maximisation.
Human-based Approach Tonella et al. combined Case-Based Reasoning (CBR) with test caseprioritisation [137]. They utilised a machine learning technique called boosting, which is a framework
to combine simple learners into a single, more general and effective learner [140]. They adopted aboosting algorithm for ranking learning called Rankboost [141]. The algorithm takes a test suite, T ,an initial prioritisation index, f , and a set of pair-wise priority relations between test cases, Φ, as
input. The pair-wise priority relation is obtained from comparisons of test cases made by the humantester. The output is a ranking function H : T → R such that, with test cases t1 and t2 , t1 ≺ t2 if
H(t1 ) > H(t2 ). The ranking function H is then used to prioritise test cases.
They used the statement coverage metric and the cyclomatic complexity computed for the functions
executed by test cases as the initial prioritisation index. The test suite of the space program wasconsidered. In order to measure the human effort required for the learning process, different test suite
sizes were adopted, ranging from 10 to 100 test cases. The results were compared to other prioritisation
techniques including optimal ordering, random prioritisation, statement coverage prioritisation, andadditional statement coverage prioritisation (the latter two correspond to statement-total and statementadditional respectively).
The results showed that, for all test suite sizes, the CBR approach was outperformed only by the
optimal ordering. The number of pair-wise relations entered manually showed a linear growth against
the size of test suites. Tonella et al. reported that for test suites of space with fewer than 60 test cases,
the CBR approach can be more efficient than other prioritisation techniques with limited human effort.
Note that empirical evaluation was performed based on an ideal user model, i.e. it was assumed thatthe human tester always makes the correct decision when comparing test cases. One notable weaknessof this approach was that it did not scale well. The input from the human tester becomes inconsistentbeyond a certain number of comparisons, which in turn limits the size of the learning samples for CBR.
Yoo et al. tried to improve the scalability of human-based prioritisation approaches by combining
pair-wise comparisons of test cases with a clustering technique [138]. While the prioritisation is stillbased on the comparisons made by the human tester, the tester is presented with clusters of similar test
cases instead of individual test cases. The prioritisation between clusters (inter-cluster prioritisation)is, therefore, performed by the tester. However, the prioritisation within each cluster (intra-cluster
prioritisation) is performed based on coverage. After both layers of prioritisation are complete, the finalordering of test cases is determined by selecting the test case with the highest priority, determined byintra-cluster prioritisation, from the next cluster in the order determined by inter-cluster prioritisation,
until all the clusters are empty. This is called the Interleaved Clusters Prioritisation (ICP) technique.
With the use of clustering, Yoo et al. were able to reduce the size of the prioritisation problem sothat they could apply a more expensive pair-wise approach called Analytic Hierarchy Process (AHP).

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AHP is a pair-wise comparison technique developed by the Operations Research community [142] andhas been successfully applied to Requirements Engineering [143]. The combination of AHP and ICP
has been empirically evaluated for programs and test suites of various sizes, using a more realistic usermodel (with errors). The results showed that this combination of techniques can be much more effective
than coverage-based prioritisation. One surprising finding was that sometimes, an error rate higher than
50%, i.e. the human tester making wrong comparisons half the time, did not prevent this techniquefrom achieving higher APFD than coverage-based prioritisation. Yoo et al. explained this unexpected
finding by showing that a certain amount of improvement derived from the effect of clustering. Thisconfirms the argument of Leon and Podgurski about the benefits of distribution-based approach [136];
the clustering sometimes enables the early execution of a fault-revealing test case that would have been
assigned low priority due to its low contribution to code coverage.
Probabilistic Approach Kim and Porter proposed a history based approach to prioritise test cases thatare already selected by regression test selection [144]. If the number of test cases selected by an RTS
technique is still too large, or if the execution costs are too high, then the selected test cases may have to
be further prioritised. Since the relevance to the recent change in SUT is assumed by the use of an RTS
technique, Kim et al. focus on the execution history of each test case, borrowing from statistical quality
control. They define the probabilities of each test case tc to be selected at time t as Ptc,t (Htc , α), where
Htc is a set of t timed observations {h1 , . . . , ht } drawn from previous runs of tc and α is a smoothing
constant. Then the probability Ptc,t (Htc , α) is defined as follows:
P0 = h1
Pk = αhk + (1 − α)Pk−1 (0 ≤ α ≤ 1, k ≥ 1)
Different definitions of Htc result in different prioritisation approaches. For example, Kim et al.
define Least Recently Used (LRU) prioritisation by using test case execution history as Htc with α
value that is as close to 0 as possible. The empirical evaluation showed that the LRU prioritisation
approach can be competitive in a severely constrained testing environment, i.e. when it is not possible
to execute all test cases selected by an RTS technique.
Mirarab and Tahvildari took a different probabilistic approach to test case prioritisation using
Bayesian Networks [145]. The Bayesian Network model is built upon changes in program elements,
fault proneness of program elements and probability of each test case to detect faults. Mirarab andTahvildari extended the approach by adding a feedback route to update the Bayesian Network asprioritisation progresses [146]. For example, if a test case covers a set of program elements, theprobability of selecting other test cases that cover the same elements will be lowered. Note that thiscorresponds to the ‘additional’ approach described by Rothermel et al. [115, 121].
History-based Approach Sherriff et al. presented a prioritisation technique based on association
clusters of software artefacts obtained by a matrix analysis called singular value decomposition [147].
The prioritisation approach depends on three elements: association clusters, relationship between test
cases and files and a modification vector. Association clusters are generated from a change matrix
using SVD; if two files are often modified together as a part of a bug fix, they will be clusteredinto the same association cluster. Each file is also associated with test cases that affect or execute
it. Finally, a new system modification is represented as a vector in which the value indicates whether

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a specific file has been modified. Using the association clusters and the modification vector, it is thenpossible to assign each file with a priority that corresponds to how closely the new modification matches
each test case. One novel aspect of this approach is that any software artefact can be considered forprioritisation. Sherriff et al. noted that the faults that are found in non-source files, such as media files
or documentation, can be as severe as those found in source code.
Requirement-based Approach Srikanth et al. presented requirement-based test case prioritisation [139]. Test cases are mapped to software requirements that are tested by them, and thenprioritised by various properties of the mapped requirements, including customer-assigned priority
and implementation complexity. One potential weakness of this approach is the fact that requirement
properties are often estimated and subjective values. Krishnamoorthi and Sahaaya developed a similar
approach with additional metrics [148].
Model-based Approach Korel et al. introduced a model based prioritisation approach [149–151].
Their initial approach was called selective prioritisation, which was strongly connected to RTS [149].
Test cases were classified into a high priority set, T SH , and a low priority set, T SL . They defined andcompared different definitions of high and low priority test cases, but essentially a test case is assigned
high priority if it is relevant to the modification made to the model. The initial selective prioritisation
process consists of the random prioritisation of T SH followed by the random prioritisation of
T SL . Korel et al. developed more sophisticated heuristics based on the dependence analysis of themodels [150, 151].
Other Approaches The use of mutation score for test case prioritisation has been analysed by
Rothermel et al. along with other structural coverage criteria [115,121]. Hou et al. considered interfacecontract mutation for the regression testing of component-based software and evaluated it with theadditional prioritisation technique [152].
Sampath et al. presented the prioritisation of test cases for web applications [153]. The test cases are,in this case, recorded user sessions from the previous version of the SUT. Session-based test cases arethought to be ideal for testing web applications because they tend to reflect the actual usage patternsof real users, thereby making for realistic test cases. They compared different criteria for prioritisationsuch as the number of HTTP requests per test case, coverage of parameter values, frequency of visits forthe pages recorded in sessions and the number of parameter values. The empirical evaluations showedthat prioritised test suites performed better than randomly ordered test suites, but also that there is nota single prioritisation criterion that is always best. However, the 2-way parameter-value criterion, theprioritisation criterion that orders tests to cover all pair-wise combinations of parameter-values between
pages as soon as possible, showed the highest APFD value for 2 out of 3 web applications that werestudied.
Fraser and Wotawa introduced a model-based prioritisation approach [154]. Their prioritisation
technique is based on the concept of property relevance [155]. A test case is relevant to a model
property if it is theoretically possible for the test case to violate the property. The relevance relation
is obtained by the use of a model-checker, which is used as the input to the greedy algorithm. Whilethey showed that property-bsed prioritisation can outperform coverage-based prioritisation, they notedthat the performance of property-based prioritisation is heavily dependent on the quality of the model
specification.

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A few techniques and analyses used for test suite minimisation or regression test selection problem
have been also applied to test case prioritisation. Rummel et al. introduced a prioritisation techniquebased on data-flow analysis by treating each du pair as a testing requirement to be covered [156].
Smith et al. introduced a prioritisation technique based on a call-tree model, which they also used
for test suite minimisation [26]. They prioritised test cases according to the number of call-tree paths
covered by each test case. Jeffrey and Gupta prioritised test cases using relevant slices [157], which was
also used for regression test selection [54]. Each test case was associated with output statements, fromwhich relevant slices were calculated. Then test cases were prioritised according to the sum of twoelements: the size of the corresponding relevant slice and the number of statements that are executedby the test case but do not belong to the relevant slice. Both elements were considered to correlate to
the chance of revealing a fault introduced by a recent change.
5.4.

Cost-Aware Test Case Prioritisation

Unlike test suite minimisation and regression test selection, the basic definition of test caseprioritisation does not involve filtering out test cases, i.e. it is assumed that the tester executes theentire test suite following the order given by the prioritisation technique. This may not be feasible inpractice due to limited resources. A number of prioritisation techniques addressed this problem of theneed to be cost-aware [23, 24, 118, 158].
With respect to cost-awareness, the basic APFD metric has two limitations. First, it considers all
faults to be equally severe. Second, it assumes that every test case costs the same in resources. Elbaum
et al. extended the basic APFD metric to APFDc so that the metric incorporates not just the rate offault detection but also the severity of detected faults and the expense of executing test cases [118]. An
ordering of test cases according to the APFDc metric detects more severe faults at a lower cost. Moreformally, let T be the set of n test cases with costs t1 , . . . , tn , and let F be the set of m faults withseverity values f1 , . . . , fm . For ordering T 0 , let T Fi be the order of the first test case that reveals theith fault. APFDc of T 0 is calculated as following:Pm
AP F Dc =Pn
1
i=1 (fi × (j=T Fi tj − 2 tT Fi ))PnPm
i=1 ti ×
i=1 fiElbaum et al. applied random ordering, additional statement coverage prioritisation, additional
function coverage prioritisation and additional fault index prioritisation techniques to space, whichcontains faults discovered during the development stage. They adopted several different models of
test case cost and fault severity, including uniform values, random values, normally distributed values
and models taken from the Mozilla open source project. The empirical results were achieved bysynthetically adding cost severity models to space. This enabled them to observe the impact ofdifferent severity and cost models. They claimed two practical implications. With respect to test casecost, they proposed the use of many small test cases rather than a few large test cases. Clearly the
number of possible prioritisations is higher with a test suite that contains many small test cases,
compared to one with a small number of large test cases. It was also claimed that having different
models of fault severity distribution can also impact the efficiency of testing. This is true only when
the prioritisation technique considers the fault detection history of previous tests.

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Elbaum et al. compared two different severity distribution models: linear and exponential. In thelinear model, the severity values grow linearly as the severity of faults increase, whereas they growexponentially in the exponential model. If the previous fault detection history correlates to the fault
detection capability of the current iteration of testing, the exponential model ensures that test caseswith a history of detecting more severe faults are executed earlier.
Walcott et al. presented a time-aware prioritisation technique [23]. Time-aware prioritisation does
not prioritise the entire test suite; it aims to produce a subset of test cases that are prioritised and canbe executed within the given time budget. More formally, it is defined as following:
Given: A test suite, T , the collection of all permutations of elements of the power set of permutationsof T , perms(2T ), the time budget, tmax , a time function time : perms(2T ) → R, and a fitness
function f it : perms(2T ) → R:
Problem: Find the test tuple σmax ∈ perms(2T ) such that time(σmax ) ≤ tmax and∀σ 0 ∈ perms(2T ) where σmax 6= σ 0 and time(σ 0 ) ≤ tmax , f it(σmax ) > f it(σ 0 ).
Intuitively, a time-aware prioritisation technique selects and prioritises test cases at the sametime so that the produced ordered subset yields higher fault detection rates within the given timebudget. Walcott et al. utilised a genetic algorithm, combining selection and prioritisation into a single
fitness function. The selection component of the fitness function is given higher weighting so thatit dominates the overall fitness value produced. The results of the genetic algorithm were comparedto random ordering, reverse ordering and the optimal ordering. The results showed that time-aware
prioritisation produces higher rates of fault detection compared to random, initial, and reverse ordering.
However, Walcott et al. did not compare the time-aware prioritisation to the existing, non time-aware
prioritisation techniques. Note that non time-aware prioritisation techniques can also be executed in‘time-aware’ manner by stopping the test when the given time budget is exhausted.
While Yoo and Harman studied test suite minimisation [24], their multi-objective optimisation
approach is also relevant to the cross-cutting concern of cost-awareness. By using multi-objective
optimisation heuristics, they obtained a Pareto-frontier which represents the trade-offs between the
different criteria, including cost. When there is a constraint on cost, the knowledge of Pareto-frontier
can provide the tester with more information to achieve higher coverage. The tester can then prioritise
the subset selected by observing the Pareto-frontier.
The cost-constraint problem has also been analysed using Integer Linear Programming (ILP) [159,160]. Hou et al. considered the cost-constraint in web service testing [159]. Users of web services are
typically assigned with a usage quota; testing a system that uses web services, therefore, has to consider
the remaining quota for each web service. The ILP approach was later analysed in more generic context
using execution time of each test as a cost factor [160].
Do and Rothermel studied the impact of time constraints on the cost-effectiveness of existingprioritisation techniques [158]. In total, six different prioritisation approaches were evaluated: originalorder, random order, total block coverage, additional block coverage, Bayesian Network approachwithout feedback, Bayesian Network approach with feedback. They considered four different time
constraints, each of which allows {25%, 50%, 75%, 100%} of time required for the execution of all test
cases. Each prioritisation approach was evaluated under these constraints using a cost-benefit model.
The results showed that, although time constraints affect techniques differently, it is always beneficial

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to adopt some prioritisation when under time constraints. The original ordering was always affected
the most severely.

6.

Meta-Empirical Studies

Recently, the meta-empirical study of regression testing techniques has emerged as a separate subject
in its own right. It addresses cross-cutting concerns such as cost-benefit analysis of regression testing
techniques and the studies of evaluation methodology for these techniques. Both studies seek toprovide more confidence in efficiency and effectiveness of regression testing techniques. Work inthese directions is still in the early stages compared to the bodies of work available for minimisation,
selection or prioritisation techniques. However, it is believed that these studies will make significant
contributions towards the technology transfer.
Empirical evaluation of any regression testing technique is inherently a post-hoc process thatassumes the knowledge of a set of known faults. Without the a-priori knowledge of faults, it would not
be possible to perform a controlled experiment comparing different regression testing techniques. Thisposes a challenge to the empirical evaluation of techniques, since the availability of fault data tends tobe limited [161].
Andrews et al. performed an extensive comparison between real faults and those seeded bymutation [161]. One concern when using mutation faults instead of real faults is that there is no
guarantee that the detection of mutation faults can be an accurate predictor of the detection of real
faults. After considering various statistical data such as the ratio and distribution of fault detection,
Andrews et al. concluded that mutation faults can indeed provide a good indication of the fault detection
capability of the test suite, assuming that mutation operators are carefully selected and equivalentmutants are removed. However, they also note that, while mutation faults were not easier to detectthan real faults, they were also not harder to detect. Do and Rothermel extended this study by focusing
the comparison on the result of test case prioritisation techniques [162, 163]. Here, they considered
whether evaluating prioritisation techniques against mutation faults and seeded faults differs. Based on
the comparison of these two evaluation methods, it was concluded that mutation faults can be safely
used in place of real or hand-seeded faults.
Although it was not their main aim, Korel et al. made an important contribution to the empiricalevaluation methodology of regression testing techniques through the empirical evaluation of theirprioritisation techniques [149–151]. They noted that, in order to compare different prioritisation
techniques in terms of their rate of fault detection, they need to be evaluated using all possible
prioritised sequences of test cases that may be generated by each technique. Even deterministic
prioritisation algorithms, such as the greedy algorithm, can produce different results for the same test
suite if some external factors change; for example, if the ordering of the initial test suite changes, there
is a chance that the greedy algorithm will produce a different prioritisation result. Korel et al. argued,
therefore, that the rate of fault detection should be measured in average across all possible prioritised
sequences. They introduced a new metric called Most Likely average Position (MLP), which measures
the average relative position of the first test case that detects a specific fault.
Elbaum et al. extended the empirical studies of prioritisation techniques with the Siemens suite andspace [116] by performing statistical analysis of the variance in APFD [117]. The APFD values wereanalysed against various program, change, and test metrics. Program metrics included mean number of

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executable statements, mean function size across all functions, etc. Change metrics included numberof functions with at least one changed statement, number of statements inserted or deleted, etc. Test
metrics included number of tests in the test suite, percentage of tests reaching a changed function,mean number of changed functions executed by a test over a test suite, etc. The aim was to identify
the source of variations in results. Elbaum et al. reported that the metrics that reflected normalised
program characteristics (such as mean function size across the program) and characteristics of test
suites in relation to programs (such as mean percentage of functions executed by a test over a test
suite) were the primary contributors to the variances in prioritisation. While they reported that thisfinding was not the anticipated one, it showed that the prioritisation results are the product of closely
coupled interactions between programs under test, changes and test suites.
Empirical evaluation of different techniques can benefit from a shared evaluation framework.
Rothermel and Harrold presented a comparison framework for RTS techniques [45], which was used
to compare different RTS techniques [1]. While minimisation and prioritisation techniques lack sucha framework, certain metrics have been used as a de facto standard evaluation framework. Rate of
reduction in size and rate of reduction in fault detection capability have been widely used to evaluate
test suite minimisation techniques [12,18–21,25,26,38–40,43]. Similarly, Average Percentage of Fault
Detection (APFD) [116] has been widely used to evaluate prioritisation techniques [23, 116, 117, 120,
121, 123, 127, 136–138, 145, 146, 154, 156, 157, 159, 160, 162–165].
Rothermel et al. studied the impact of test suite granularity and test input grouping on the costeffectiveness of regression testing [120, 165]. They first introduced the concept of test grains, which isthe smallest unit of test input that is executable and checkable. Test cases are constructed by grouping
test grains. Based on this, they defined test suite granularity as the number of test grains in a test
case, and test input grouping as the way test grains are added to each test case, e.g. randomly orgrouped by their functionality. They reported that having a coarse grained test suite did not significantly
compromise the fault detection capability of the test suite, but resulted in decreased total execution
time. The savings in execution time can be explained by the fact that a coarse grained test suite
contains fewer test cases, thereby reducing the set-up time and other overheads that occur between
execution of different test cases. However, they did not consider the cost of the test oracle. It is not
immediately obvious whether the cost of a test oracle would increase or decrease as the test suite
granularity increases. This oracle cost could affect the overall cost-effectiveness.
Kim et al. studied the impact of test application frequency to the cost-effectiveness of RTS
techniques [166, 167]. Their empirical studies showed that the frequency of regression test application
has a significant impact on the cost-effectiveness of RTS techniques. They reported that RTS techniques
tend to be more cost-effective when the frequency of test application is high. It implies that only a small
amount of changes are made between tests, which makes RTS more effective. However, as intervals
between tests grows, changes are accumulated and RTS techniques tend to select more and more test
cases, resulting in low cost-effectiveness. One interesting finding is that, as intervals between tests
grow, random re-testing tends to work very well. With small testing intervals, the random approach
fails to focus on the modification. As testing intervals increase, more parts of SUT need to be re-tested,
improving the effectiveness of the random approach. Elbaum et al. studied the impacts of changes interms of the quantitative nature of modifications [164]. They investigated how the cost-effectivenessof selection and prioritisation techniques is affected by various change metrics such as percentage ofchanged lines of code, average number of lines of code changed per function, etc. Their empiricalanalysis confirmed that the differences in these metrics can make a significant impact to the cost-

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effectiveness of techniques. However, they also reported that simple size of change, measured in lines
of code, was not a predominant factor in determining the cost-effectiveness of techniques. Rather, it
was the distribution of changes and the ability of test cases to reach these changes.
Elbaum et al. also presented a technique for selecting the most cost-effective prioritisation
technique [168]. They applied a set of prioritisation techniques to the same set of programs, andanalysed the resulting APFD metric values. Different techniques perform best for different programs;
they applied the classification tree technique to predict the best-suited technique for a program.
Note that the term ‘cost-effectiveness’ in this work means the efficiency of a prioritisation techniquemeasured by the APFD metric; the computational cost of applying these techniques was not considered.
Rosenblum and Weyuker introduced a coverage-based cost-effective predictor for RTS
techniques [169]. Their analysis is based on the coverage relation between test cases and program
entities. If each program entity has a uniformly distributed probability of being changed in the next
version, it is possible to predict the average number of test cases to be selected by a safe RTS techniqueusing coverage relation information. They evaluated their predictor with the TestTube RTS tool [62],using multiple versions of the KornShell [170] and an I/O library for Unix, SFIO [171], as subjects.
Their predictor was reasonably accurate; for example, it predicted an average of 87.3% of the test
suite to be selected for KornShell, when TestTube selected 88.1%. However, according to the costmodel of Leung and White [172], the cost of coverage analysis for RTS per test case was greaterthan the cost of execution per test case, indicating that TestTube was not cost-effective. Harrold
et al. introduced an improved version of the cost-effective predictor of Rosenblum et al. for moreaccurate cost-effectiveness prediction of version-specific RTS [173]. They evaluated their predictor
using TestTube and another RTS tool, DejaVu [57].
Modelling the cost-effectiveness of regression testing techniques has emerged as a research topic.
This is motivated by the observation that any analysis of cost-effectiveness should depend on somemodel. Leung and White introduced an early cost-model for regression testing strategies and compared
the cost models of the retest-all strategy and the selective retesting strategy [172]. Malishevsky etal. presented detailed models of cost-benefit trade-offs for regression testing techniques [119]. They
applied their models to the regression testing of bash, a popular Unix shell [174], with differentf, where f is the cost of omitting one fault, e is the additional cost per test
ratio values of e+c
and c is the result-validation cost per test. The results implied that if a regression testing techniquedoes not consider f , it may overestimate the cost-effectiveness of a given technique. The costmodel of Malichevsky et al. has been extended and evaluated against the prioritisation of JUnit test
cases [175]. Smith and Kapfhammer studied the impact of the incorporation of cost into test suite
minimisation [176]. Existing minimisation heuristics including HGS [12], delayed greedy [18] and2-optimal greedy algorithm [128] were extended to incorporate the execution cost of each test case.
Do and Rothermel considered the impact of time constraints on selection and prioritisation techniques
across multiple consecutive versions of subject programs to incorporate software life-cycle factors into
the study [177].
Reflecting the complexity of regression testing process, cost-effectiveness models often need to besophisticated in order to incorporate multiple variables [119, 175–177]. However, complexity can be abarrier to uptake. Do and Rothermel introduced an approach based on statistical sensitivity analysis tosimplify complicated cost models [178]. Their approach fixed certain cost factors that were deemed tobe the least significant by the sensitivity analysis. The empirical evaluation showed that, while certain
levels of simplification can still preserve the accuracy of the model, over-simplification may be risky.

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Summary & Discussion

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Analysis of Current Global Trends in the Literature
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onselection(RTS)12and test case prioritisation. This number includes papers on methodologies of empiricalevaluation and comparative studies. Data summarising the results in these papers are shown in
Tables6IV, V, VI and VII in Appendix. Note that the last category consists of papers on cross-cutting
concerns for empirical studies, such as methodologies of empirical evaluation and analyses of costeffectiveness,
as well as purely comparative studies and surveys. Figure 2 plots the number of surveyed0
papers for each year since 1977, when Fischer published his paper on regression test selection using
a linear programming approach [47]. The observed trend in the number of publications shows that the
Minimisation
Selection
Prioritisation
Empirical/Comparative
field continues to grow.
Figure 3 shows the chronological trend in the number of studies for each of the topics in thispaper. In this figure, the papers have been classified into four different categories. The first three
categories contain papers on minimisation, selection and prioritisation respectively. The fourth categorycontains papers on empirical evaluation and comparative studies, including previous surveys. Papers
that consider more than one subject are represented in each category for which they are relevant; forexample, a survey on RTS [1] is counted in both the selection category and the comparative studies
category. Therefore, while the graph closely resembles Figure 2, it is not a representation of the numberof publications. Rather, the figure should be read as a guide to the trends in study topics over time.
Considering that many different RTS approaches were introduced in the late 80s and 90s, recent
research on RTS techniques has been mostly concerned with the application and evaluation of the
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on minimisation

Fischer et al. 1977
on selection

Minimisation

Selection

Prioritisation

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

0

1978

6

1977

Empirical/Comparative
Augmentation
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
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0
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0
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1
0
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3
0
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0
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0
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1
1
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4
2
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5
2
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2
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0
7
3
1
8
0
0
6
5
2
4
1
0
47
27

8

Empirical/Comparative

Figure 3. Relative research interest in each subject. Papers that consider more than one subject were counted
multiple times.

graph walk approach [83,84,87,90–95]. As the figure reveals, the interest in test case prioritisation has
been steadily growing since the late 90s. In Figures 2 and 3, the data for 2009 is, of course, partial.
Whereas most of the early papers on RTS were theoretical (see Table V), empirical evaluation ofregression testing techniques has recently received a burgeoning interest. Not only are there more
publications on pure empirical/comparative studies (as can be observed in Figure3), but recent studies
of regression testing techniques tend to evaluate the suggested techniques empirically, as can beobserved in Table IV, V and VI.
However, the scale of empirical studies seems to remain limited. Figure 4 shows the maximum sizeof SUTs (measured in Lines of Code) and test suites (measured as the number of test cases) studied
empirically in the literature. For both data, only the empirical studies that explicitly note the size of
subject SUTs and test suites have been included. When only the average size of test suites is given,
the maximum average size of studied test suites has been used. For the maximum size of SUTs, only
the empirical studies that use source code as test subjects have been included; for example, studies ofregression testing of UML models are not included. For about 60% of empirical studies, the largest
SUT studied is smaller than 10,000 LoC. For about 70% of empirical studies, the largest test suitestudied contains fewer than 1,000 test cases.
Figure 5 shows the origins of subject SUTs studied in the literature. For detailed information
about the classification, refer to the Appendix. Programs available from the Software Infrastructure
Repository (SIR) [179] account for over 50% of subjects of empirical studies of regression testing
techniques. The predominant programming language is C, followed by Java. Considering that the first
paper appeared in 2002, model-based techniques have shown significant growth. Although there are

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1

Maximum Size of SUT Observed In Literature (LoC)
10^3~10^4
37%

1
1

1

> 10^514%

1~10^323%

1

10^4~10^525%

REGRESSION TESTING MINIMISATION, SELECTION AND PRIORITISATION : A SURVEY

Other Java

1
Max. SUT
1
Size (LOC)
1~10^3 1
1
10^3~10^4

Web service

UML/Other
Model

1
1

1

1

1

21
34
23
13

1
10^4~10^5> 10^51~10^3
23%

1
1

1
1

1

> 10^5
14%

Count
16
27
22
14
14

Max. Size of Test Suite Observed In Literature

Maximum Size of SUT Observed In Literature (LoC)Count

Max. Test
1 Suite Size
~100
100~500
500~1000
1000~3000
3000~

1

Max. Test
Suite Size
~100
100~500
500~1000
1000~3000
3000~

Count

39

1
10^3~10^4
37%
16

27
22
14
14

1

~100
17%

10^4~10^5
25%

100~500
29%

1
1 1

Max. Size of Test Suite Observed In Literature1> 10^5100~500
29% 14%1~10^323% 1

1

1Max. Test
Suite Size
~100
100~500
500~1000
3000~
1000~3000
15%
3000~

1

1

1

1
1
1
1

1

1

1
1

1

1

1

1

1

1

1

1
1
1

1

1

1

1

1

1
1
1

1

1

1
1

1

1
1
1

1

~100
17%

1

Count

Origin of Subject SUT Observed In Literature
Other C/C++/C# Programs
19%

500~1000
24%

1000~3000 1
15%Max. Size of Test
Suite Observed In Literature

Subject TypeCount
Siemens
32
Suite
Space
22
1
SIR Unix
9
100~500
Utilities
Originof
Subject
SUT
Observed
In
Literature
SIR Java
11
29%
Programs~100
Other C/C++/C# Programs
Other C/C++/
25
17%
19%
C# Programs
Other Java
18
Other Java Programs
Programs
SIR Java Programs
14%
UML/Model/
12
Count
8%
Other
32
Languages
3000~
500~1000
Web
2
1 24%
15% 22
Appliction
SIR Unix Utilities
1
91000~3000
1SIR Java Programs
8%

Other Java Program
14%

1

1

1
1

1

1

Figure 4. Maximum size of SUT and test suites studied in literature

16
27
22
1 14
1 14

1

1

1000~3000
15%

1

1

1

500~100024%

SIR Unix Utilities
7%

Space
17%

UML/Model/Othe
9%

Subject Type
Siemens Suite
Siemens
24%
Suite
Space
SIR Unix
Utilities
1
15% 7%
UML/Model/Other Languages
SIR Java
111
9%Programs
Other C/C++/
25
C# Programs
Web Appliction
Other Java
18
Origin of Subject SUT Observed In Literature2%
ProgramsSpace
UML/Model/
12
Other C/C++/C# Programs
17%
Other
1
1
19%
Siemens Suite
Languages
24% Other Java Programs
Web
2
SIR Java Programs
14%
Appliction
Subject Type
Count
1
8%
1
Siemens
32
Figure 5. Origins of subject SUTs observed in the literature.
Suite
Space
22
SIR Unix Utilities
SIR Unix
9
7%
1 Utilities
UML/Model/Other Languages
SIR Java
11
9%
Programs
Other C/C++/
25
1
C# Programs
Web Appliction
Other Java
18
2%
Programs
1
Space
Copyright c 2007 Shin Yoo & MarkHarmanSoftw. Test. Verif. Reliab. 2007; 00:1–7
UML/Model/
12117%Prepared using stvrauth.cls
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Other
1
Siemens Suite
Languages
24%
1
Web
2
Appliction
1

1

1

3000~
15%

1

Web Applict
2%

40

S.YOO AND M. HARMAN

several papers on RTS for web applications [91–95], empirical evaluation of these techniques remains
limited.
7.2.
7.2.1.

State-of-the-Art, Trends and Issues
State-of-the-Art

Among the class of RTS techniques, the graph walk approach seems to be the predominant techniquein the literature. Although it was originally conceived for Control Dependence Graphs and Program
Dependence Graphs [46, 56], the most widely used form of graph walk approach works on Control
Flow Graphs [57, 79]. Its popularity can be observed from the fact that the approach has been applied
to many forms of graph representation of SUT that are not CFGs [82–84, 87]. Indeed, the strength of
the graph walk approach lies not only in the fact that it is intuitive but also in the generic applicabilityof the technique to any graph representation of systems.
By studying the literature, it becomes clear that two ideas played essential roles in the developmentof RTS techniques: Leung and White’s early idea of regression testing and test case classification [10],and Rothermel and Harrold’s definition of a safe regression test selection [45]. Collectively, these twoideas provided a profound theoretical framework that can be used to evaluate RTS techniques.
The existence of such a theoretical framework is what differentiate RTS techniques from test suite
minimisation and test case prioritisation. For RTS techniques, it is possible to define what a safe
technique should do because the RTS problem is specifically focused on the modifications between
two versions of SUT. Minimisation and prioritisation techniques, on the other hand, are forced torely on surrogate metrics for real fault detection capability. Therefore, it may not be clear what ‘safe’
minimisation or prioritisation techniques would mean. A minimisation technique would not be safe
unless the surrogate metric perfectly captures the fault detection capability of the test suite; however,
the empirical studies so far have shown that there is no such single metric. For prioritisation, the conceptof ‘safe’ and ‘non-safe’ does not apply directly since the aim of the technique is to permute the test
suite and not to select a subset out of it.
Naturally, the history of minimisation and prioritisation literature is an on-going exploration ofdifferent heuristics and surrogate metrics. It is interesting to note that the greedy algorithm, a good
approximation for the set cover problem and, therefore, test suite minimisation problem, is also anefficient heuristic for test case prioritisation precisely because of its greedy nature. In other words,
as much as possible, as soon as possible. As a result of this, the greedy approach and its variations
have a strong presence in the literature on both test suite minimisation [11, 14, 18] and test caseprioritisation [115, 116, 121]. Recently, there are other approaches to test suite minimisation andtest case prioritisation that aim to overcome the uncertainty of surrogates, e.g. the use of multiple
minimisation criteria [21,22,24] and the use of expert knowledge for test case prioritisation [137,138].
7.2.2.

TrendsEmphasis on Models: Whereas most of the early regression testing techniques concerned code-based,
white-box regression testing, the model-based regression testing approach has been of more recent
growing interest [32–34, 68, 69, 88, 107–111]. UML models and Extended Finite State Machines

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41(EFSMs) are often used.
New Domains: While the majority of existing literature on regression testing techniques concerns
stand-alone programs written in C and Java, there are a growing number of other domains that are
being considered for regression testing. For example, spreadsheets [76], GUIs [25, 180] and web
applications [91–95, 153] have been considered.
Multi-criteria Regression Testing: In minimisation and prioritisation, it is known that there isno single surrogate metric that correlates to fault prediction capability for all programs [8, 38–
40, 115, 121]. One potential way of overcoming this limitation is to consider multiple surrogates
simultaneously [21, 22] using classical multi-objective optimisation techniques, such as the weighted
sum approach [21] or the prioritised approach [22]. Expert domain knowledge has also been used inaddition to software metrics for test case prioritisation [137,138]. Finally, Multi-Objective Evolutionary
Algorithms (MOEAs) have been used to deal with multiple objectives [24].
Cost-awareness: The cost of regression testing is a cross-cutting concern for all three classes ofregression testing techniques. Studies of cost-aware regression testing can be classified into two
categories. First, there is work that aims to incorporate the cost of testing directly into regression testing
techniques at technical level; for example, cost-aware test case prioritisation [23, 118] or minimisation
techniques that provide the tester with a series of alternative subsets of the original test suites that canbe executed in different amounts of time [24]. Second, there are empirical evaluations of regression
testing techniques that consider whether the application of these techniques is indeed cost-effective inthe wider context of the overall software lifecycle [120, 158, 165, 169, 177].
7.2.3.

IssuesLimited Subjects: In Figure 5, subject programs from SIR [179] account for almost 60% of thesubjects of empirical studies observed in the regression testing technique literature. While this iscertainly evidence that SIR has been of tremendous value to the research community, it also meansthat many regression testing techniques are being evaluated against a limited set of programs and test
suites. By no means is this a criticism of SIR itself or work that is based on subjects from SIR; rather,
the dependency on SIR shows how time consuming and difficult it is to collect multiple versions ofprogram source code, their test suites and associated fault information, let alone to make it publicly
available. The SIR commonality also supports and facilitates comparisons. However, the community
faces a possible risk of ‘over-fitting’ the research of these techniques to those programs that are easily
available. Open source software projects are often suggested as an alternative source of data, but fromthe results of this survey analysis it seems that their use for research on regression testing techniques islimited.
Two potential approaches to the issue of limited subjects can be envisioned. The first approach is
to design a method that will allow a realistic simulation of real software faults. There is early work inthis direction. Andrews et al. considered whether mutation faults are similar to real faults [161]; Do
and Rothermel studied the same question especially in the context of regression testing [163]. Jia andHarman used a search-based approach to obtain higher-order mutation faults that are more ‘subtle’ and,
therefore, potentially harder to detect than first order mutation faults [181].

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The second approach is to engage more actively with industry and open source communities. This
is not an easy task, because the information about testing and software faults tends to be sensitive,
particularly to commercial organisations. The research community faces the challenge of convincing
the wider practitioner community of the cost-effectiveness and usefulness of these techniques. This is
closely related to the issue of technology transfer, to which the paper now turns.
Technology Transfer: A detailed observation of the literature suggests that the community may
have reached a stage of maturity that, in order to progress to the next level of achievement,
technology transfer to industry will play an important role. While the research community may nothave found the ‘silver bullet’, most empirical evaluation of the proposed techniques suggests thatapplication of minimisation, selection and prioritisation techniques does make a difference from 1)
uncontrolled regression testing, i.e. retest-all approach and un-prioritised regression testing, and 2)
random approaches.
However, empirical evaluation and application of regression testing techniques at industrial levelseems to remain limited [182]. Out of the 159 papers listed in Table IV, V, VI and VII, only 31 papers
list a member of industry as an author or a co-author. More importantly, only 12 papers consider
industrial software artefacts as a subject of the associated empirical studies [68, 69, 88, 99–102, 104–
106, 126, 147]. This suggests that a large scale industrial uptake of these techniques has yet to occur.

8.

Future Directions

This section discusses some of the possible future directions in the field of regression testing
techniques. While it is not possible to predict the future direction a field of study will follow, it waspossible to identify some trends in literature, which may suggest and guide the direction of future
research.
8.1.

Orchestrating Regression Testing Techniques with Test Data Generation

Automatic test data generation has made advances in both functional and non-functional testing [183].
Superficially test data generation is the counterpart to regression testing techniques; it creates test cases
while regression testing seeks to mange them. However, because these two activities are typicallylocated at the opposite ends of the testing process, they may become close when the testing processrepeats.
Orchestrating regression testing with test data generation has a long heritage. From the classificationof test cases by Leung and White [10], it follows that regression testing involves two different needs thatare closely related to test data generation: repairing obsolete test cases for corrective regression testing
and generating additional test data for progressive regression testing. The second problem, in particular,
has been referred to as the test suite augmentation problem [184, 185]. There was early work on bothproblems. Memon and Soffa considered automatic repair of GUI test cases that were made obsolete by
the changes in the GUI [180], which was later extended by Memon [186]. Similarly, Alshahwan andHarman focused on the use of user session data for regression testing of web applications, and how thetest cases can be automatically repaired for the next version [187]. Apiwattanapong et al. identified the

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43

testing requirements that are needed to test the new and modified parts of a SUT so that additional test
data can be generated automatically [184, 185].
However, it is believed that there are additional areas that may be synergetic. For example, test data
generation might possibly refer to the test cases selected and unselected during the last iteration inorder to identify the part of the SUT to focus on. Similarly, regression testing techniques can use theadditional information provided by test data generation techniques in order to make regression testing
more efficient and effective. For example, there are test data generation techniques that target a specificconcern in the SUT, such as detection of the presence of a memory leak. The additional information
about the intention behind each test case could be used to enrich the minimisation, selection andprioritisation process.

8.2.

Multi-Objective Regression Testing

Regression testing is a complex and costly process that may involve multiple objectives and constraints.
For example, the cost of executing a test case is usually measured as the time taken to execute the test
case. However, there may be a series of different costs involved in executing a test case, such as setting
up the environment or preparing a test input, each of which may be subject to a different constraint.
Existing techniques also assume that test cases can be executed in any given order without any change
to the cost of execution, which seems unrealistic. Test cases may have dependency relations between
them. It may also be possible to lower the cost of execution by grouping test cases that share the sametest environment, thereby saving set-up time.
Considering the complexity of real-world regression testing, existing representations of problems
in regression testing may be over-simplistic. Indeed, most of the published empirical studies rely on
relatively small-scale academic examples. Even when real-world programs are studied, they tend to be
individual programs, not a software system as a whole. Larger software systems do not simply entail
larger problem size; they may denote a different level of complexity.
It is believed that, in order to cope with the complexity of regression testing, regression testing
techniques may need to become multi-objective. There is existing, preliminary work that seeks torepresent the cost of test case execution as an additional constraint using evolutionary algorithms [23,24]. Multi-objective optimisation heuristics may provide the much-needed flexibility that is required
for the representation of problems with high complexity.
Another benefit of moving to a multi-objective paradigm is the fact that it provides additional
insight into the regression testing problem by allowing the testers to observe the inherent trade-offs
between multiple constraints. This is not possible with so called classical approaches to multi-objective
problems that either consider one objective at a time [22] or conflate multiple objectives into a single
objective using weighting [21,23]; these approaches may be based on multiple criteria, but they are nottruly multi-objective in the sense that they all produce a single solution. The result of a multi-objective
optimisation is often a set of solutions that do not dominate each other, thereby forming the trade-offs
between constraints. The insight into the trade-offs may provide additional information that is hard to
obtain manually.

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8.3.

Problem of Test Oracle and Its Cost

Test oracles present a set of challenging problems for software testing. It is difficult to generatethem automatically, they often require human efforts to verify and the cost of this effort is hard to
estimate and measure. The oracle cost has been considered as a part of cost models [178], but has
not been considered as a part of the process of minimisation, selection and prioritisation itself. Since
regression testing techniques seek to efficiently re-use existing test cases, information about the cost ofverifying the output observed with the existing test suite may be collected across versions. This can beincorporated into the existing regression testing techniques.
While a test oracle and its cost may be seen as yet another additional objective that can be consideredusing a multi-objective approach, it can be argued that the test oracle will present many interesting andexciting research questions in the context of regression testing and, thus, deserves a special treatment in
its own right. This is because, compared to other testing cost such as the physical execution time of test
cases, the test oracle cost is closely related to the quality of testing. Moreover, unlike some costs thatcan be reduced by using more advanced hardware, the cost of oracle verification derives from humaneffort and is, therefore, harder to reduce. These characteristics make the issues related to test oracles
challenging but interesting research subjects.

8.4.

Consideration of Other Domains

The majority of regression testing techniques studied in this survey concern white-box structuralregression testing of code-level software artefacts. However, other domains are emerging as new andexciting research subjects.
Recently, Service Oriented Architectures (SOAs) have been of keen interest both for academic
researchers and industrialists. In the SOA paradigm, software system is built, or composed, byorchestrating a set of web services, each of which takes charge of a specific task. Several approaches
have been introduced to address the issue of regression testing of web services, most of which seek toapply the same technique developed for traditional applications to web services [91–95]. However, theinherently distributed nature of an SOA system presents several challenges that are alien to traditional
regression testing techniques.
Web services often reside in remote locations and are developed by a third-party, making it
hard to apply the traditional white-box regression testing techniques that require analysis of sourcecode. Modifications can happen across multiple services, which can make fault localisation difficult.
High interactivity in web applications may result in complex test cases that may involve humaninteraction. Finally, distributed systems often contain concurrency issues. Traditional regression testing
techniques assume that the program produces deterministic output. This may not be adequate fortesting applications with concurrency. Answers to these specific issues in regression testing of web
applications are still in the early stage of development.
Model-based regression testing techniques have also received growing interests [68, 69, 107–111].
It is believed that the model-based regression testing techniques will be of crucial importance in the
future for the following reasons:

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45• Higher level regression testing: these techniques can act as a medium between
requirement/specification and testing activities, bringing regression testing from the structurallevel to functional level.
• Scalability: in dealing with software systems of industrial scale, model-based techniques will
scale up better than code-based techniques.
However, there are a few open research questions. First, there is the well known issue of traceability.
Unless the traceability from requirements and specifications to code-level artefacts and test cases isprovided, the role of model-based regression testing techniques will be severely limited. Second, thereis the issue of test adequacy: if a test adequacy A is appropriate for a model M , which test adequacyshould be used to test the program P that has been automatically generated from M ? Does A still
apply to P ? If so, does it follow that M being adequate for A means P will be adequate for A as well?
There are also other interesting domains to consider. Testing of GUIs has received growing interest,
not only in the context of regression testing [25, 135, 180], but also in the context of testing in
general [188, 189]. Regression testing GUIs presents a different set of challenges to code-based
structural regression testing since GUIs are usually generated in a visual programming environment;
they are often subject to frequent changes and, not being well-typed, do not readily facilitate static
analysis.
8.5.

Non-functional Testing

A majority of existing regression testing techniques rely upon structural information about the SUT,
such as data flow analysis, CFG analysis, program slices and structural coverage. The impact that
non-functional property testing will have on regression testing techniques has not been fully studied.
Existing techniques were able to map the problems in the regression testing to well-formed abstract
problems using the properties of structural information. For example, test suite minimisation could
be mapped to the minimal hitting set problem or the set coverage problem, precisely because thetechniques were based on the concept of ‘coverage’. Similarly, graph-walking approaches to test caseselection were made possible because the changes between different versions were defined by structuraldifferences in CFGs.
Imagine regression testing techniques for non-functional properties. What would be the minimised
test suite that can test the power consumption of an embedded system? How would test cases beprioritised to achieve an efficient and effective stress testing of a web application? These questions
remain largely unanswered and may require approaches that are significantly different from existing
paradigms.
8.6.

Tool Support

Closely related to the issue of technology transfer is the issue of tool support. Without readily
available tools that implement regression testing techniques, practical adoption will remain limited.
One potential difficulty of providing tool support is the fact that, unlike unit testing for which there
exists a series of frameworks based on the xUnit architecture, there is not a common framework forthe regression testing process in general. The closest to a common ground for regression testing would
be an Integrated Development Environment (IDE), such as Eclipse, with which the xUnit architecture

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S.YOO AND M. HARMAN

is already integrated successfully. A good starting point for regression testing techniques may be themanagement framework of unit test cases, built upon xUnit architecture and IDEs.

9.

Conclusion

This paper provides both a survey and a detailed analysis of trends in regression test case selection,
minimisation and prioritisation. The paper shows how the work on these three topics is closely related
and provides a survey of the landscape of work on the development of these ideas, their applications,empirical evaluation and open problems for future work.
The analysis of trends reported in the paper reveals some interesting properties. There is evidence
to suggest that the topic of test case prioritisation is of increasing importance, judging by the shift
in emphasis towards it that is evident in the literature. It is also clear that the research community ismoving towards assessment of the complex trade offs and balances between different concerns, withan increase in work that considers the best way in which to incorporate multiple concerns (cost andvalue for instance) and to fully evaluate regression testing improvement techniques.
This focus on empirical methodology is one tentative sign that the field is beginning to mature. The
trend analysis also indicates a rising profile of publication, providing evidence to support the claim thatthe field continues to attract growing attention from the wider research community, which is a positive
finding for those working on regression test case selection and minimisation and, in particular thoseworking on prioritisation problems.
Our survey also provides evidence to indicate that there is a preponderance of empirical work thatdraws upon a comparatively small set of subjects (notably those available through the SIR repository).
This is a testament to the importance of this source of case study material. It is valuable because it
allows for cross comparison of results and replication, which is essential for the development of any
science. However, it may potentially suggest a risk of over-fitting.
Acknowledgements: Shin Yoo is supported by the EPSRC SEBASE project (EP/D050863). MarkHarman is supported by EPSRC Grants EP/D050863, GR/S93684 & GR/T22872, by EU grant IST33472 (EvoTest) and also by the kind support of DaimlerChrysler Berlin and Vizuri Ltd., London.

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Press, 2007; 174–183.
153. Sampath S, Bryce RC, Viswanath G, Kandimalla V, Koru AG. Prioritizing user-session-based test cases for web
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154. Fraser G, Wotawa F. Test-case prioritization with model-checkers. SE’07: Proceedings of the 25th conference on IASTEDInternational Multi-Conference, ACTA Press: Anaheim, CA, USA, 2007; 267–272.
155. Fraser G, Wotawa F. Property relevant software testing with model-checkers. SIGSOFT Software Engineering Notes 2006;
31(6):1–10.
156. Rummel M, Kapfhammer GM, Thall A. Towards the prioritization of regression test suites with data flow information.
Proceedings of the 20th Symposium on Applied Computing (SAC 2005), ACM Press, 2005.
157. Jeffrey D, Gupta N. Test case prioritization using relevant slices. Proceedings of the 30th Annual International Computer
Software and Applications Conference (COMPSAC 2006), IEEE Computer Society: Washington, DC, USA, 2006; 411–
420.
158. Do H, Mirarab SM, Tahvildari L, Rothermel G. An empirical study of the effect of time constraints on the cost-benefitsof regression testing. Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software
Engineering, ACM Press, 2008; 71–82.
159. Hou SS, Zhang L, Xie T, Sun JS. Quota-constrained test-case prioritization for regression testing of service-centric
systems. Proceedings of IEEE International Conference on Software Maintenance (ICSM 2008), IEEE Computer Society
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161. Andrews JH, Briand LC, Labiche Y. Is mutation an appropriate tool for testing experiments? Proceedings of the 27th
International Conference on Software Engineering (ICSE 2005), ACM Press, 2005; 402–411.
162. Do H, Rothermel G. A controlled experiment assessing test case prioritization techniques via mutation faults. Proceedingsof the 21st IEEE International Conference on Software Maintenance (ICSM 2005), IEEE Computer Society Press, 2005;
411–420.
163. Do H, Rothermel G. On the use of mutation faults in empirical assessments of test case prioritization techniques. IEEE
Transactions on Software Engineering 2006; 32(9):733–752.
164. Elbaum S, Kallakuri P, Malishevsky A, Rothermel G, Kanduri S. Understanding the effects of changes on the costeffectiveness of regression testing techniques. Software Testing, Verification, and Reliability 2003; 13(2):65–83.
165. Rothermel G, Elbaum S, Malishevsky A, Kallakuri P, Qiu X. On test suite composition and cost-effective regression
testing. ACM Transactions on Software Engineering and Methodology July 2004; 13(3):277–331.
166. Kim JM, Porter A, Rothermel G. An empirical study of regression test application frequency. Proceedings of the 22ndInternational Conference on Software Engineering (ICSE 2000), ACM Press, 2000; 126–135.
167. Kim JM, Porter A, Rothermel G. An empirical study of regression test application frequency. Software Testing,
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168. Elbaum S, Rothermel G, Kanduri S, Malishevsky AG. Selecting a cost-effective test case prioritization technique.
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169. Rosenblum D, Weyuker E. Using coverage information to predict the cost-effectiveness of regression testing strategies.
IEEE Transactions on Software Engineering March 1997; 23(3):Page(s):146 – 156.
170. Bolsky MI, Korn DG. The New KornShell Command and Programming Language. Prentice Hall PTR: Upper Saddle
River, NJ, USA, 1995.
171. Korn D, phong Vo K. SFIO: Safe/Fast String/File IO. Proceedings of the Summer Usenix Conference 1991, 1991; 235–256.
172. Leung HKN, White L. A cost model to compare regression test strategies. Proceedings of the International Conference
on Software Maintenance (ICSM 1991), IEEE Computer Society Press, 1991; 201–208.
173. Harrold MJ, Rosenblum DS, Rothermel G, Weyuker EJ. Empirical studies of a prediction model for regression test
selection. IEEE Transactions on Software Engineering 2001; 27(3):248–263.
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176. Smith AM, Kapfhammer GM. An empirical study of incorporating cost into test suite reduction and prioritization.
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on Foundations of Software Engineering, ACM Press, 2006; 141–151.
178. Do H, Rothermel G. Using sensitivity analysis to create simplified economic models for regression testing. Proceedings of
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179. Do H, Elbaum SG, Rothermel G. Supporting controlled experimentation with testing techniques: An infrastructure andits potential impact. Empirical Software Engineering 2005; 10(4):405–435.
180. Memon AM, Soffa ML. Regression testing of GUIs. ESEC/FSE-11: Proceedings of the 9th European software
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181. Jia Y, Harman M. Constructing subtle faults using higher order mutation testing. Proceedings of IEEE International
Workshop on Source Code Analysis and Manipulation (SCAM 2008), IEEE Computer Society Press, 2008; 249–258.
182. Harrold M, Orso A. Retesting software during development and maintenance. Frontiers of Software Maintenance (FoSM
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185. Santelices RA, Chittimalli PK, Apiwattanapong T, Orso A, Harrold MJ. Test-suite augmentation for evolving software.
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186. Memon AM. Automatically repairing event sequence-based gui test suites for regression testing. ACM Transactions on
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187. Alshahwan N, Harman M. Automated session data repair for web application regression testing. Proceedings of 2008
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USA, 2008; 298–307.
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REGRESSION TESTING MINIMISATION, SELECTION AND PRIORITISATION : A SURVEY55

Appendix
The following tables contain detailed information about publications on each class of technique: test
suite minimisation in Table IV, Regression Test Selection (RTS) in Table V, test case prioritisation in
Table VI and empirical/comparative studies in Table VII. Note that publications that consider morethan one class of techniques appear in multiple tables for ease of reference. For example, a survey ofRegression Test Selection [1] appears in both Table V and Table VII.
Information on the maximum size of SUT (measured in Lines of Code) and the maximum size of test
suites were collected from papers only when they were explicitly noted. Some papers that consideredmultiple test suites for a single program contained only the average, in which case the size of the test
suite with largest average size was recorded. When the studied SUT cannot be measured in Lines ofCode (LOC), the detailed information was provided in footnotes. Tables also contain information about
the origins of the studied SUT, which are classified as follows:• Siemens suite [41]: all or part of the following set of C programs - printtokens,printtokens2, schedule, schedule2, replace, tcas, totinfo, available from
SIR [179].
• space: an interpreter for Array Description Language (ADL), developed by European Space
Agency. Available from SIR.
• Unix utilities in SIR: all or part of the following set of C programs - flex, grep, gzip, sed,vim, bash, available from SIR• Java programs in SIR: all or part of the following set of Java programs - siena, ant,
jmeter, jtopas, xml-security, nanoxml, available from SIR• Other C/C++/C# programs: programs written in C/C++/C# that are not available from SIR• Other Java programs: programs written in Java that are not available from SIR• Models and programs in other languages : models including state machines and UML
diagrams. There are also a very few empirical studies that consider programs written in otherlanguages, e.g. Pascal.
• Web applications : web applications and web services

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∗
†
‡

Web
Applications

Models & Other
Languages

Other Java
Programs

Other C/
C++/C#

26
19
36.8
260
33
200
1,168
1,985
260
169
5,542
5,542
1,985
135
4,712
539
183
1,560
1,500
N/A
169
1,500
13,585
N/A
5,542
110

SIR Java
Programs

1,000
N/A
48
N/A∗
516
842
6,218
TheoryTheory65,632
68,782
516
TheoryTheory
6,218
516
512
68,782
516
6,218
6,218
Theory
5,500
6,218
N/A†
11,803
1,455
6,218
11,803
6,218
26,824
1,892,226
N/A‡
6,822

SIR Unix
Utilities

Max. Test
Suite Size

1992
1993
1995
1996
1998
1998
1999
2000
2002
2002
2002
2002
2002
2003
2003
2003
2004
2004
2005
2005
2006
2007
2007
2007
2007
2007
2007
2007
2008
2008
2008
2009
2009
2009

space

Year

Horgan et al. [13]Harrold et al. [12]Offutt et al. [14]Chen et al. [11]Rothermel et al. [38]
Wong et al. [39]
Wong et al. [40]Schroeder et al. [31]Korel et al. [33]Malishevsky et al. [119]Rothermel et al. [120]Rothermel et al. [8]Vaysburg et al. [32]Anido et al. [35]Harder et al. [27]Marre et al. [17]Black et al. [21]Rothermel et al. [165]
Jeffrey et al. [19]McMaster et al. [43]Tallam et al. [18]Chen et al. [34]Hou et al. [152]
Jeffrey et al. [20]Leitner et al. [29]McMaster et al. [42]Smith et al. [26]Yoo et al. [24]McMaster et al. [25]Yu et al. [44]Zhong et al. [6]Hsu et al. [22]Kaminski et al. [36]Smith et al. [176]Siemens
Suite

ReferenceMax. SUT
Size (LoC)

Table IV: Summary of publications on test suite minimisation

•
•
•
•
•
•

•
•

•

•

•

•

•
•
•

•

•
•

•
•

•

•

•

•
•
•

•

•
•
•

•

•
•
•

•
•

Chen et al. [11]1 evaluated their heuristics using simulation rather than real data.
Leitner et al. [29]2 minimised the length of a unit test case, not a test suite.
Kaminski et al. [36]3 applied logical reduction to a set of 19 boolean predicates taken from avionic software.

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REGRESSION TESTING MINIMISATION, SELECTION AND PRIORITISATION : A SURVEY

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Web
Applications

Models & Other
Languages

Other Java
Programs

Other C/
C++/C#

235
26
39
N/A
5,542
-‡‡
1,033
N/A
5,542
5,542
1,000
N/A
398
5,542
100
4,361
N/A
317
1,033
189
19
4,712
138
596
306
493
1,168
1,985
1,168
N/A
1,985
707
N/A
4,361
N/A

SIR Java
Programs

TheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheoryTheory550
1,000
TheoryTheoryTheoryTheoryTheoryTheoryTheory11,000
TheoryTheoryTheoryN/A∗Survey516
623
49,316
N/A†
516
512
6,218
N/A‡
Theory
516
516
6,218
Theory6,218
N/A§
24,849Theory49,316
N/A
516
6,218
6,035
N/A¶
N/A
N/Ak
65,632
68,782
65,632
TheoryN/A∗∗Theory68,782532,000
N/A∗∗
6,218
902

SIR Unix
Utilities

Max. Test
Suite Size

1977
1981
1987
1988
1988
1989
1989
1989
1989
1989
1990
1990
1990
1990
1990
1991
1992
1992
1992
1993
1993
1993
1993
1994
1994
1994
1995
1995
1996
1996
1997
1997
1997
1997
1997
1997
1997
1998
1998
1998
1998
1999
2000
2000
2000
2001
2001
2001
2001
2001
2001
2002
2002
2002
2002
2002
2003
2003
2003
2004
2004
2004
2004
2005
2005

space

Year

Fischer [47]
Fischer et al. [48]Yau et al. [53]Benedusi et al. [61]Harrold et al. [50]Harrold et al. [74]Harrold et al. [51]Hartmann et al. [71]Leung et al. [10]Taha et al. [52]Hartmann et al. [73]Hartmann et al. [72]Lee et al. [70]Leung et al. [63]Leung et al. [64]Horgan et al. [15]Gupta et al. [49]
Laski et al. [67]White et al. [65]Agrawal et al. [54]Bates et al. [60]Rothermel et al. [46]White et al. [66]Chen et al. [62]Rothermel et al. [56]Rothermel et al. [45]Binkley [96]Kung et al. [98]Rothermel et al. [1]Rothermel [55]Binkley [97]Rosenblum et al. [5]Rosenblum et al. [169]
Rothermel et al. [57]Rothermel et al. [78]
Wong et al. [75]Vokolos et al. [58]Ball [80]Graves et al. [7]Rothermel et al. [79]Vokolos et al. [59]Harrold [114]Kim et al. [166]
Le-Traon et al. [110]Rothermel et al. [81]Beydeda et al. [85]Bible et al. [4]Harrold et al. [82]Harrold et al. [173]Jones et al. [124]Orso et al. [89]Briand et al. [68]Chen et al. [88]Fisher II et al. [76]Malishevsky et al. [119]Rothermel et al. [120]Elbaum et al. [164]Wu et al. [111]White et al. [101]Deng et al. [107]Rothermel et al. [165]Orso et al. [86]White et al. [99]Kim et al. [167]Martins et al. [87]Siemens
Suite

ReferenceMax. SUT
Size (LoC)

Table V: Summary of publications on Regression Test Selection (RTS)

•
•

•

•

•
•
•

•
•
•
•

•
•
•
•
•

•

•

•

•
•
•
•
•

•

•
•
•
•
•
•
•
•

•

•

•

•

•
•
•

•
•

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58

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∗
†
‡
§
¶

k
∗∗
††
‡‡

Web
Applications

Models & Other
Languages

Other Java
Programs

Other C/
C++/C#

N/A
1,533
N/A
52
592
592
N/A
567
63
592
31
N/A
323,614SIR Java
Programs

N/A¶
1,200,000
80,400
N/A††
N/A
TheoryTheory757,000
757,000
N/A¶
Theory6,035
TheoryTheoryTheoryTheory3,423
757,000
757,000
SurveyOver 1MLoC
N/A¶

SIR Unix
Utilities

Max. Test
Suite Size

2005
2005
2006
2006
2006
2006
2006
2006
2006
2006
2007
2007
2007
2007
2007
2007
2007
2007
2007
2008
2008
2009

space

Year

Muccini et al. [112]Skoglund et al. [106]Do et al. [177]Lin et al. [91]Pilskalns et al. [108]Tarhini et al. [94]Zhao et al. [84]Zheng et al. [102]Zheng et al. [103]Muccini et al. [113]
Farooq et al. [109]Orso et al. [90]
Ruth et al. [92]
Ruth et al. [95]
Ruth et al. [93]Sherriff et al. [147]Xu et al. [83]Zheng et al. [104]Zheng et al. [105]Fahad et al. [3]White et al. [100]Briand et al. [69]Siemens
Suite

ReferenceMax. SUT
Size (LoC)

Table V: Summary of publications on Regression Test Selection (RTS)

•
•
•
•

•
•
•
•

•

•
•
•
•
•

Kung et al. applied their technique to InterView C++ library, which contains 147 files and over 140 classes [98].
Rosenblum and Weyuker [169] evaluated their cost-effectiveness predictor using 31 versions of the KornShell and asingle version of the SFIO (Unix library), but exact versions, sizes of SUT and sizes of test suites were not specified.
Vokolos et al. evaluated their textual difference selection technique using a small C function in addmon family of tools,
power [58].
Le-Traon et al. [110] presented a case study of a model of packet-switched data transport service, the size of which was
not specified.
Briand et al. studied UML models rather than real systems, the biggest of which contained either 9 classes with 70
methods [68] or 32 classes with 64 methods [69]. Muccini et al. studied an RTS technique at software architecture leveland presented case studies for the architecture model of an elevator system and a cargo router system [112, 113].
Fisher II et al. [76] evaluated their retesting strategy for spreadsheets with a spreadsheet containing 48 cells, 248 expressions
and 100 predicates.
White et al. applied the firewall approach to GUI program with 246 GUI objects [101]. White and Robinson applied thefirewall approach to a real time system developed by ABB [99].
Lin et al. [91] applied their technique to a Java Interclass Graph (JIG) with over 100 nodes.
Binkley [97] applied his technique to five C programs (the largest of which had 623 statements), but only identified thestatements that require retesting without considering test suites.

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REGRESSION TESTING MINIMISATION, SELECTION AND PRIORITISATION : A SURVEY

59

∗

†
‡
§
¶

•
•
•
•
•
•
•

Web
Applications

Models & Other
Languages

Other Java
Programs

Other C/
C++/C#

19
169
169
166
4,712
169
169
226
1,168
1,985
3,128
1,168
3,333
877
1,985.32
1,985
877
980
21
50
1,533
877
877
N/A
169
53
246
183
1,000
4,350
105
796
N/A
912
1,000
1,439
912
975
890
N/A
110
1,061
209

SIR Java
Programs

Theory
516
6,218
6,218
6,218
6,218
6,218
6,218
6,218
65,632
68,78218,000,000∗
65,632
N/A†
80,40068,00068,782
N/A‡
80,400
800
N/A§
2,500
N/A‡
80,40080,40080,400
516
6,218
1,808
N/A‡
N/A§
5,500
1,416
11,148124,000
17,155
1,455
80,400N/A¶
1,416
80,400107,992
9,401
6,000
6,822
122,169
5,361

SIR Unix
Utilities

Max. Test
Suite Size

1999
1999
2000
2001
2001
2001
2001
2002
2002
2002
2002
2002
2003
2003
2004
2004
2004
2005
2005
2005
2005
2005
2006
2006
2006
2006
2006
2006
2006
2007
2007
2007
2007
2007
2007
2007
2007
2008
2008
2008
2008
2008
2008
2009
2009
2009
2009

space

Year

Harrold [114]Rothermel et al. [115]Elbaum et al. [116]Elbaum et al. [117]Elbaum et al. [118]Jones et al. [124]Rothermel et al. [121]Elbaum et al. [123]Kim et al. [144]Malishevsky et al. [119]Rothermel et al. [120]Srivastava et al. [126]Elbaum et al. [164]
Leon et al. [136]Do et al. [127]Elbaum et al. [168]Rothermel et al. [165]
Bryce et al. [131]Do et al. [162]
Korel et al. [149]Rummel et al. [156]Srikanth et al. [139]Bryce et al. [132]Do et al. [177]Do et al. [175]Do et al. [163]
Jeffrey et al. [157]
Tonella et al. [137]Walcott et al. [23]Bryce et al. [135]Fraser et al. [154]Hou et al. [152]
Korel et al. [150]Li et al. [128]Mirarab et al. [145]Qu et al. [133]Smith et al. [26]Do et al. [158]
Hou et al. [159]
Korel et al. [151]Mirarab et al. [146]Qu et al. [134]Sampath et al. [153]
Krishnamoorthi et al. [148]Smith et al. [176]Yoo et al. [138]Zhang et al. [160]Siemens
Suite

ReferenceMax. SUT
Size (LoC)

Table VI: Summary of publications on test case prioritisation

•
•
•
•
•
•
•
•
•

•
•

•
•

•

•
•
•

•
•

•
•
•
•
•
•
•
•
•
•
•
•
•

•
•
•
•
•

•

•
•
•
•
•
•

•
•

Srivastava and Thiagarajan [126] considered the biggest software system so far, an office productivity application with over
18 million LoC. However, the technique took the executable binary as input, not the source code. The compiled application
was 8.8Mb in size, with a 22Mb symbol table.
Leon et al. [136] considered three compilers: javac, jikes and gcc. The sizes of the source code were not specified.
Bryce et al. [131, 132, 135] studied interaction coverage prioritisation, for which the LoC metric is not appropriate.
These papers considered models rather than real software systems. Rummel et al. [156] applied their technique to a modelwith 3 classes and 21 methods. Fraser et al. [154] did not specify the size of the studied models and test suites.
Hou et al. [159] evaluated their technique using a web application composed of 12 web services.

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∗

•
•
•
•
•
•

Web
Applications

Models & Other
Languages

Other Java
Programs

•

•
•

•
•
•
•
•
•

•

Other C/
C++/C#

1,033
N/A
5,542
398
4,361
1,033
19
1,168
1,985
1,168
1985.32
1,985
877
4,361
1,533
877
877
912
912
N/A
110

SIR Java
Programs

TheoryTheorySurvey49,316
N/A∗
512
516
6,218
49,316516
65,632
68,782
65,63268,00068,78280,400
Theory6,218
80,40080,40080,40080,40080,400
Systematic Review
Survey26,824
6,822

SIR Unix
Utilities

Max. Test
Suite Size

1991
1994
1996
1997
1997
1997
1998
2000
2001
2001
2002
2002
2003
2004
2004
2005
2005
2005
2006
2006
2006
2008
2008
2008
2008
2008
2009

space

Year

Leung et al. [172]Rothermel et al. [45]Rothermel et al. [1]Rosenblum et al. [5]Rosenblum et al. [169]
Rothermel et al. [78]Graves et al. [7]Kim et al. [166]
Bible et al. [4]Harrold et al. [173]Malishevsky et al. [119]Rothermel et al. [120]Elbaum et al. [164]Elbaum et al. [168]Rothermel et al. [165]
Do et al. [162]
Do et al. [179]Kim et al. [167]
Do et al. [177]Do et al. [175]Do et al. [163]
Do et al. [158]
Do et al. [178]
Engström et al. [2]Fahad et al. [3]Zhong et al. [6]Smith et al. [176]Siemens
Suite

ReferenceMax. SUT
Size (LoC)

Table VII: Summary of publications on empirical evaluation & comparative studies

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•
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•
•
•
•
•

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•
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Rosenblum et al. [169] evaluated their cost-effectiveness predictor using 31 versions of the KornShell and a single
version of the SFIO (Unix library), but exact versions, sizes of SUT and sizes of test suites were not specified.

Copyright c 2007 Shin Yoo & Mark Harman
Prepared using stvrauth.cls

Softw. Test. Verif. Reliab. 2007; 00:1–7
DOI: 10.1002/stvr