Mutant reduction based on dominance relation for weak mutation testing

Gong, Dunwei; Zhang, Gongjie; Yao, Xiangjuan; Meng, Fanyong

doi:10.1016/j.infsof.2016.05.001

Cited by 36 publications

(23 citation statements)

References 46 publications

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“…[131] explored the program path space and selected mutants that are as diverse as possible with respect to the paths covering them. Gong et al [132] selected mutants that structurally dominate the others (covering them results in cover-ing all the others). This work aims at weak mutation and attempts to statically identify dominance relations between the mutants.…”

Section: Mutant Reduction Strategiesmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

357

343

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Mutation testing realises the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases and to support experimentation.Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a 'mini-handbook'-style roadmap for the application of mutation testing as experimental methodology.

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Section: Mutant Reduction Strategiesmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

357

343

View full text Add to dashboard Cite

show abstract

“…Sun et al (2017) suggested selecting mutants that are diverse in terms of static control flow graph paths that cover them. Gong et al (2017) used code dominator analysis in order to select mutants that, when they are covered, maximize the coverage of other mutants. This work applies weak mutation and attempts to identify dominance relations between the mutants in a static way.…”

Section: Related Workmentioning

confidence: 99%

Selecting fault revealing mutants

et al. 2019

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Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of 'static' program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings).

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“…In our previous research [27], we transformed the mutants into the mutant branches to reflect whether the corresponding mutants are weakly killed or not, and formed a new program by inserting all the mutant branches into the original program; and the nondominated ones, corresponding to the mutants after reduction, were obtained after analyzing the dominance relations among the mutant branches. Our previous method can further improve the weak mutation testing by only covering the nondominated mutant branches, and the generated test cases can cover all the mutant branches, that is, weakly kill all the mutants.…”

Section: Correlation Analysismentioning

confidence: 99%

Mutant Selecting According to the Nondominated Original Statements

Zhang

Xie

Dong

et al. 2019

Scientific Programming

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Mutation testing is a technique for evaluating the quality of a test suite. However, the costly computation from a large number of mutants affects the practical application of mutation testing, and reducing the number of mutants is reasonably an efficient way for mutation testing. We propose a new method for reducing mutants by analyzing dominance between statements in the program under test. The proposed method only selects the mutants generated from the nondominated statements, and the mutants generated from the dominated statements are reduced. After applying the proposed method to nine programs, the experimental results show that our method reduces over 75% mutants and well maintains the mutation adequacy.

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Mutant reduction based on dominance relation for weak mutation testing

Cited by 36 publications

References 46 publications

Mutation Testing Advances: An Analysis and Survey

Mutation Testing Advances: An Analysis and Survey

Selecting fault revealing mutants

Mutant Selecting According to the Nondominated Original Statements

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