Bi-criteria models for all-uses test suite reduction

Black, Jennifer; Melachrinoudis, Emanuel; Kaeli, David

doi:10.1109/icse.2004.1317433

Cited by 95 publications

(107 citation statements)

References 14 publications

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“…After a program is modified, developers often reuse existing tests for the program before modification and may add some tests for the modification. As it is time-consuming to run the aggregated tests, many test selection and/or reduction techniques [5,21,65,71] have been proposed to reduce the number of tests used in regression testing. To optimize the cost spent on regression testing, test prioritization techniques [61,62,68] have been proposed to schedule the execution order of tests.…”

Section: Regression Testingmentioning

confidence: 99%

“…Most research in test selection, reduction and prioritization investigates various coverage criteria, including statement coverage, function coverage [12], modified condition/decision coverage [44], and so on. Other research investigates various test selection, reduction, and prioritization algorithms, including greedy algorithms [25], genetic algorithms [35], integer linear programming based algorithms [5,21,71], and so on.…”

Section: Regression Testingmentioning

confidence: 99%

See 1 more Smart Citation

Is This a Bug or an Obsolete Test?

Hao

Lan

Zhang

et al. 2013

ECOOP 2013 – Object-Oriented Programming

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Abstract. In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests.

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Section: Regression Testingmentioning

confidence: 99%

Section: Regression Testingmentioning

confidence: 99%

Is This a Bug or an Obsolete Test?

Hao

Lan

Zhang

et al. 2013

ECOOP 2013 – Object-Oriented Programming

View full text Add to dashboard Cite

show abstract

“…. , a k ) ∈ TS we define a counting function d (1) tc , which assigns to each action a ∈ A the number of times it is called by tc:…”

Section: Formalizationmentioning

confidence: 99%

“…Further work on how to apply 0/1-Integer linear programming to the test suite reduction problem [12] or how to improve the Greedy heuristics [5,6,2] can be found. In [1,14] there are approaches using multi-objective optimization functions, whereas in [8] an approach based on genetic algorithms is introduced. Some empirical results for test suite reductions have been reported in [11].…”

Section: Introductionmentioning

confidence: 99%

Variations over Test Suite Reduction

Güttinger

Kozyura

Kremer

et al. 2013

Testing Software and Systems

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Abstract. This paper deals with the problem of effective test suite reduction. In its original form this problem is equivalent to the set covering problem, which has already been extensively studied and many strategies such as greedy or branch and bound for computation of an approximative optimal solution to this NP-complete problem are known. All of these algorithms only focus on one objective which is the minimization of the number of action calls within the test suite reduction. However, practical experience shows that balancing out the distribution of action calls is another objective which should be considered when choosing an efficient test suite. We will therefore introduce and evaluate different extensions of the standard techniques which incorporate action call distribution. We will see that these adjusted strategies can compute a reduced test suite with a smoother distribution over function calls within an acceptable amount of additional time in comparison to the classic algorithms.

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“…The works which propose a new approach commonly include heuristic algorithms [14], genetic algorithm-based techniques [18] and approaches based on integer linear programming [19].…”

Section: Test Suite Reductionmentioning

confidence: 99%

Bi-criteria Test Suite Reduction by Cluster Analysis of Execution Profiles

Khalilian

Parsa

2012

Advances in Software Engineering Techniques

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Abstract. The aim has been to minimize regression test suites while retaining fault detection capability of the test suite admissible. An appropriate minimized test suite should exercise different execution paths within a program. However, minimization of test suites may result in significant fault detection loss. To alleviate the loss, a new bi-criteria heuristic algorithm, using cluster analysis of test cases execution profiles is proposed in this paper. Cluster analysis of execution profiles categorizes test cases according to their similarity in terms of exercising a certain coverage criterion. Considering additional coverage criteria the proposed algorithm samples some test cases from each cluster. These additional criteria exercise execution paths, different from those covered by the main testing criteria. Experiments on the Siemens suite manifest the applicability of the proposed approach and present interesting insights into the use of cluster analysis to the bi-criteria test suite reduction.

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Bi-criteria models for all-uses test suite reduction

Cited by 95 publications

References 14 publications

Is This a Bug or an Obsolete Test?

Is This a Bug or an Obsolete Test?

Variations over Test Suite Reduction

Bi-criteria Test Suite Reduction by Cluster Analysis of Execution Profiles

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