Mutation testing strategies using mutant classification

Authors:
Mike Papadakis;Yves Le Traon
Affiliations:
University of Luxembourg;University of Luxembourg
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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Abstract

Mutation testing has a widespread reputation of being a rather powerful testing technique. However, its practical application requires the detection of equivalent mutants. Detecting equivalent mutants is cumbersome since it requires manual analysis, resulting in unbearable testing cost. To overcome this difficulty, researchers have proposed the use of mutant classification, an approach that helps isolating equivalent mutants. From this perspective, the present paper establishes and assesses possible mutant classification strategies. The conducted study suggests that while mutant classification can be useful in isolating equivalent mutants, it fails to kill some mutants. Indeed, the experimental results show that the proposed strategies achieve to kill approximately 95% of the introduced killable mutants.