Random Selection Might Just be Indomitable

Abstract

Mutation Testing offers a powerful approach to assessing unit test set quality; however, software developers may be reluctant to embrace the technique due to the tremendous number of mutants it generates, including redundant and equivalent mutants. Recent research indicates that redundant mutants affect a test engineer’s work effort only slightly, whereas equivalent mutants have a direct linear impact. Moreover, the time invested analyzing equivalent mutants produces no unit tests. Dominator mutants seek to address the redundancy problem, but they require the identification of all subsumption relationships, which implicitly identifies all equivalent mutants. This research study shows that, when equally informed, random selection can perform as well as dominator mutants.