Mutation reduction strategies considered harmful

Rahul Gopinath, Iftekhar Ahmed, Mohammad Amin Alipour, Carlos Jensen, Alex Groce

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Mutation analysis is a well known yet unfortunately costly method for measuring test suite quality. Researchers have proposed numerous mutation reduction strategies in order to reduce the high cost of mutation analysis, while preserving the representativeness of the original set of mutants. As mutation reduction is an area of active research, it is important to understand the limits of possible improvements. We theoretically and empirically investigate the limits of improvement in effectiveness from using mutation reduction strategies compared to random sampling. Using real-world open source programs as subjects, we find an absolute limit in improvement of effectiveness over random sampling - 13.078%. Given our findings with respect to absolute limits, one may ask: How effective are the extant mutation reduction strategies? We evaluate the effectiveness of multiple mutation reduction strategies in comparison to random sampling. We find that none of the mutation reduction strategies evaluated - many forms of operator selection, and stratified sampling (on operators or program elements) - produced an effectiveness advantage larger than 5% in comparison with random sampling. Given the poor performance of mutation selection strategies - they may have a negligible advantage at best, and often perform worse than random sampling - we caution practicing testers against applying mutation reduction strategies without adequate justification.

Original languageEnglish (US)
Article number7942146
Pages (from-to)854-874
Number of pages21
JournalIEEE Transactions on Reliability
Volume66
Issue number3
DOIs
StatePublished - Sep 2017

Keywords

  • Mutation analysis
  • software testing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

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