Time-aware test-case prioritization using integer linear programming

Zhang, Lu; Hou, Shu-Jin; Guo, Chuanbin; Xie, Tao; Mei, Hong

doi:10.1145/1572272.1572297

Cited by 122 publications

(96 citation statements)

References 43 publications

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“…In addition, previous research has shown that it is often appropriate to use automatically mutated faults for regression-testing experimentation [22], [24], [25]. In fact, Andrews et al [24], [25] found that for software-testing experimentation mutated faults are [20], which provides an API for solving mixed integer linear programming problems and has been widely used in other studies [5], [9].…”

Section: Dependent Variables and Metricsmentioning

confidence: 99%

“…For example, an industrial collaborator of Rothermel et al [1] reported that running the entire test suite for one of their software products takes nearly seven weeks. Therefore, researchers have developed various techniques to reduce the cost of regression testing through test-suite reduction [2]- [4], test-case prioritization [1], [5], [6], and regression test selection [7]. Yoo and Harman [8] presented a detailed survey of regression testing techniques.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Study of JUnit Test-Suite Reduction

Zhang

Marinov

et al. 2011

2011 IEEE 22nd International Symposium on Software Reliability Engineering

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Abstract-As test suites grow larger during software evolution, regression testing becomes expensive. To reduce the cost of regression testing, test-suite reduction aims to select a minimal subset of the original test suite that can still satisfy all the test requirements. While traditional test-suite reduction techniques were intensively studied on C programs with specially generated test suites, there are limited studies for test-suite reduction on programs with real-world test suites. In this paper, we investigate test-suite reduction techniques on Java programs with real-world JUnit test suites. We implemented four representative test-suite reduction techniques for JUnit test suites. We performed an empirical study on 19 versions of four real-world Java programs, ranging from 1.89 KLoC to 80.44 KLoC. Our study investigates both the benefits and the costs of test-suite reduction. The results show that the four traditional test-suite reduction techniques can effectively reduce these JUnit test suites without substantially reducing their fault-detection capability. Based on the results, we provide a guideline for achieving cost-effective JUnit testsuite reduction.

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Section: Dependent Variables and Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Empirical Study of JUnit Test-Suite Reduction

Zhang

Marinov

et al. 2011

2011 IEEE 22nd International Symposium on Software Reliability Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among them, the aim of test case prioritization is to rank the test cases to reveal faults earlier in order to reduce the cost of testing. There are numerous test case prioritization methods proposed in the literature [2,3,4,5,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55].…”

Section: Related Workmentioning

confidence: 99%

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Ayav

2017

Software Testing Verif & Rel

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SUMMARYTest case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a costeffective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing.

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“…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. 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.…”

Section: Regression Testingmentioning

confidence: 99%

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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Time-aware test-case prioritization using integer linear programming

Cited by 122 publications

References 43 publications

An Empirical Study of JUnit Test-Suite Reduction

An Empirical Study of JUnit Test-Suite Reduction

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Is This a Bug or an Obsolete Test?

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