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A study of equivalent and stubborn mutation operators using human analysis of equivalence

Authors:

Yue JiaAuthors Info & Claims

ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

Pages 919 - 930

https://doi.org/10.1145/2568225.2568265

Published: 31 May 2014 Publication History

Abstract

Though mutation testing has been widely studied for more than thirty years, the prevalence and properties of equivalent mutants remain largely unknown. We report on the causes and prevalence of equivalent mutants and their relationship to stubborn mutants (those that remain undetected by a high quality test suite, yet are non-equivalent). Our results, based on manual analysis of 1,230 mutants from 18 programs, reveal a highly uneven distribution of equivalence and stubbornness. For example, the ABS class and half UOI class generate many equivalent and almost no stubborn mutants, while the LCR class generates many stubborn and few equivalent mutants. We conclude that previous test effectiveness studies based on fault seeding could be skewed, while developers of mutation testing tools should prioritise those operators that we found generate disproportionately many stubborn (and few equivalent) mutants.

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Cited By

Kuznetsov KGambi ADhiddi SHess JGopinath R(2024)Empirical Evaluation of Frequency Based Statistical Models for Estimating Killable MutantsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686669(61-71)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686669
Kushigian BKaufman SFeatherman RPotter HMadadi AJust RChristakis MPradel M(2024)Equivalent Mutants in the Wild: Identifying and Efficiently Suppressing Equivalent Mutants for Java ProgramsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680310(654-665)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680310
Wei CYao XGong DLiu H(2024)Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and Estimation of Distribution AlgorithmIEEE Transactions on Software Engineering10.1109/TSE.2024.335829750:3(551-573)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3358297
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Index Terms

A study of equivalent and stubborn mutation operators using human analysis of equivalence
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

cover image ACM Conferences

ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

May 2014

1139 pages

ISBN:9781450327565

DOI:10.1145/2568225

General Chair:
Pankaj Jalote
IIIT-Delhi, India
,
Program Chairs:
Lionel Briand
University of Luxembourg, Luxembourg
,
André van der Hoek
University of California, Irvine, USA

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Published: 31 May 2014

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May 31 - June 7, 2014

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Cited By

Kuznetsov KGambi ADhiddi SHess JGopinath R(2024)Empirical Evaluation of Frequency Based Statistical Models for Estimating Killable MutantsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686669(61-71)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686669
Kushigian BKaufman SFeatherman RPotter HMadadi AJust RChristakis MPradel M(2024)Equivalent Mutants in the Wild: Identifying and Efficiently Suppressing Equivalent Mutants for Java ProgramsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680310(654-665)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680310
Wei CYao XGong DLiu H(2024)Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and Estimation of Distribution AlgorithmIEEE Transactions on Software Engineering10.1109/TSE.2024.335829750:3(551-573)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3358297
Straubinger PDegenhart AFraser G(2024)An Empirical Evaluation of Manually Created Equivalent Mutants2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW60967.2024.00052(237-246)Online publication date: 27-May-2024
https://doi.org/10.1109/ICSTW60967.2024.00052
Zafar MAfzal WEnoiu EHaider ZSingh I(2024)Optimizing Model-based Generated Tests: Leveraging Machine Learning for Test Reduction2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW60967.2024.00020(44-54)Online publication date: 27-May-2024
https://doi.org/10.1109/ICSTW60967.2024.00020
AlBlwi SMarsit IKhaireddine BAyad ALoh JMili A(2024)Subsumption, correctness and relative correctness: Implications for software testingScience of Computer Programming10.1016/j.scico.2024.103177(103177)Online publication date: Jul-2024
https://doi.org/10.1016/j.scico.2024.103177
Wei CYao XGong DLiu HDang X(2024)Set evolution based test data generation for killing stubborn mutantsJournal of Systems and Software10.1016/j.jss.2024.112121216(112121)Online publication date: Oct-2024
https://doi.org/10.1016/j.jss.2024.112121
Felici RPozzi LFuria C(2024)HyperPUT: generating synthetic faulty programs to challenge bug-finding toolsEmpirical Software Engineering10.1007/s10664-023-10430-829:2Online publication date: 15-Jan-2024
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Langdon WClark D(2024)Deep imperative mutations have less impactAutomated Software Engineering10.1007/s10515-024-00475-432:1Online publication date: 3-Dec-2024
https://doi.org/10.1007/s10515-024-00475-4
Görz PMathis BHassler KGüler EHolz TZeller AGopinath RCalandrino JTroncoso C(2023)Systematic assessment of fuzzers using mutation analysisProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620491(4535-4552)Online publication date: 9-Aug-2023
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