Static analysis of mutant subsumption

Kurtz, Bob; Ammann, Paul; Offutt, Jeff

doi:10.1109/icstw.2015.7107454

Cited by 49 publications

(25 citation statements)

References 33 publications

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“…Finally, we determine all the dominated mutant branches by the above statistical approach. The automated analysis of the dominance relation between mutant branches will be a significant research task in our near future work, and related work such as Kurtz et al [46], Kintis and Malevris [52], Offutt and Pan [53], and Zhang et al [54] will be of great help in achieving this goal. Furthermore, we find that different branches have different numbers of dominated branches.…”

Section: Discussionmentioning

confidence: 99%

“…Later, Kurtz et al developed a method of building the static mutant subsumption graph (SMSG) by the symbolic execution, so as to generate test data to kill mutants. Further, they refined the SMSG to form a statically-derived dynamic mutant subsumption graph (SDMSG) by generating test data in a feedback mechanism [46].…”

Section: Testability Transformation and Mutant Reductionmentioning

confidence: 99%

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Mutant reduction based on dominance relation for weak mutation testing

Gong

Zhang

Yao

et al. 2017

Information and Software Technology

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractContext: As a fault-based testing technique, mutation testing is effective at evaluating the quality of existing test suites. However, a large number of mutants result in the high computational cost in mutation testing. As a result, mutant reduction is of great importance to improve the efficiency of mutation testing. Objective: We aim to reduce mutants for weak mutation testing based on the dominance relation between mutant branches. Method: In our method, a new program is formed by inserting mutant branches into the original program. By analyzing the dominance relation between mutant branches in the new program, the non-dominated one is obtained, and the mutant corresponding to the non-dominated mutant branch is the mutant after reduction. Results: The proposed method is applied to test ten benchmark programs and six classes from open-source projects.The experimental results show that our method reduces over 80% mutants on average, which greatly improves the efficiency of mutation testing. Conclusion: We conclude that dominance relation between mutant branches is very important and useful in reducing mutants for mutation testing.

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Section: Discussionmentioning

confidence: 99%

Section: Testability Transformation and Mutant Reductionmentioning

confidence: 99%

Mutant reduction based on dominance relation for weak mutation testing

Gong

Zhang

Yao

et al. 2017

Information and Software Technology

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“…For each mutant, this feature can determine whether the mutant is subsuming or not, which tests kill the mutant, which mutants are subsuming the mutant, and which mutants are subsumed by the mutant. It is also capable of exporting the mutant subsumption graph proposed by Kurtz et al for each project [22,23].…”

Section: Experimental Featuresmentioning

confidence: 99%

LittleDarwin: A Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems

Parsai

Murgia

Demeyer

2017

Fundamentals of Software Engineering

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Abstract. Mutation testing is a well-studied method for increasing the quality of a test suite. We designed LittleDarwin as a mutation testing framework able to cope with large and complex Java software systems, while still being easily extensible with new experimental components. LittleDarwin addresses two existing problems in the domain of mutation testing: having a tool able to work within an industrial setting, and yet, be open to extension for cutting edge techniques provided by academia. LittleDarwin already offers higher-order mutation, null type mutants, mutant sampling, manual mutation, and mutant subsumption analysis. There is no tool today available with all these features that is able to work with typical industrial software systems.

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“…Mutant subsumption is defined as the relationship between two non-equivalent mutants A and B in which A subsumes B if and only if all inputs that kill A is guaranteed to kill B [12]. The subsumption relationship for faults has been defined by Kuhn in 1999 [11], but its use for mutation testing has been popularized by Jia et al for creating hard to kill higher-order mutants [8].…”

Section: State Of the Artmentioning

confidence: 99%

“…subsumes Mutant B if and only if (i) A is killed, and (ii) every test that kills A also kills B. Kurtz et al [12] use the notion of dynamic mutant subsumption graph (DMSG) to visualize the concept of dynamic mutant subsumption. Each node in a DMSG represents a set of all mutants that are mutually subsuming.…”

Section: Mutantsmentioning

confidence: 99%

Dynamic mutant subsumption analysis using LittleDarwin

Parsai

Demeyer

2017

Proceedings of the 8th ACM SIGSOFT International Workshop on Automated Software Testing

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Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement. CCS CONCEPTS• Software and its engineering → Software testing and debugging;

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Static analysis of mutant subsumption

Cited by 49 publications

References 33 publications

Mutant reduction based on dominance relation for weak mutation testing

Mutant reduction based on dominance relation for weak mutation testing

LittleDarwin: A Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems

Dynamic mutant subsumption analysis using LittleDarwin

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