Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering 2017
DOI: 10.1145/3106237.3106280
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The care and feeding of wild-caught mutants

Abstract: Mutation testing of a test suite and a program provides a way to measure the quality of the test suite. In essence, mutation testing is a form of sensitivity testing: by running mutated versions of the program against the test suite, mutation testing measures the suite's sensitivity for detecting bugs that a programmer might introduce into the program. This paper introduces a technique to improve mutation testing that we call wild-caught mutants; it provides a method for creating potential faults that are more… Show more

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Cited by 46 publications
(36 citation statements)
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“…Stemming from the previous considerations by Brown et al [22], as well as from recent work aimed at learning bug repairs from an existing set of previous fixes [23], [24] and, more generally, from the successful applications of machine learning on code to support several SE tasks tasks [25]- [34], we conjecture that mutants can be automatically learned from previous fixes. We propose an approach for automatically learning mutants from actual bug fixes.…”
Section: Introductionmentioning
confidence: 73%
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“…Stemming from the previous considerations by Brown et al [22], as well as from recent work aimed at learning bug repairs from an existing set of previous fixes [23], [24] and, more generally, from the successful applications of machine learning on code to support several SE tasks tasks [25]- [34], we conjecture that mutants can be automatically learned from previous fixes. We propose an approach for automatically learning mutants from actual bug fixes.…”
Section: Introductionmentioning
confidence: 73%
“…This is a very conservative estimation that does not consider the mutated predictions. Brown et al [22] achieved a compilability rate of 14%. Moreover, "the majority of failed compilations (64%) arise from simple parsing errors" [22], whereas we achieve a better-estimated compilability and a high percentage of syntactically correct predictions.…”
Section: Experimental Designmentioning
confidence: 99%
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“…Brown et al [102] proposed a technique to mine mutation operators from source code repositories. The intuition of this work is that by making mutants sintactically similar to real faults one can get semantically similar mutants.…”
Section: Mutant Operatorsmentioning
confidence: 99%