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The coupling effect: fact or fiction

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A. OffuttAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 14, Issue 8

Pages 131 - 140

https://doi.org/10.1145/75309.75324

Published: 01 November 1989 Publication History

Abstract

Fault-based testing strategies test software by focusing on specific, common types of errors. The coupling effect states that test data sets that detect simple types of faults are sensitive enough to detect more complex types of faults. This paper describes empirical investigations into the coupling effect over a specific domain of software faults. All the results from this investigation support the validity of the coupling effect. The major conclusion from this investigation is that by explicitly testing for simple faults, we are also implicitly testing for more complicated faults. This gives us confidence that fault-based testing is an effective means of testing software.

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Cited By

Wang ZYu MFeng YZhang W(2024)Do we need high-order mutation in fault-based Boolean-specification testing?Journal of Systems and Software10.1016/j.jss.2023.111933210:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111933
Ahmed ZSchwass EHerbold STrautsch FGrabowski J(2024)A new perspective on the competent programmer hypothesis through the reproduction of real faults with repeated mutationsSoftware Testing, Verification and Reliability10.1002/stvr.1874Online publication date: 29-Feb-2024
https://doi.org/10.1002/stvr.1874
Mao RZhang LZhang X(2024)Mutation‐based data augmentation for software defect predictionJournal of Software: Evolution and Process10.1002/smr.263436:6Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1002/smr.2634
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Index Terms

The coupling effect: fact or fiction
1. General and reference
  1. Cross-computing tools and techniques
    1. Reliability
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
  2. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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Copyright © 1989 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 November 1989

Published in SIGSOFT Volume 14, Issue 8

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Cited By

Wang ZYu MFeng YZhang W(2024)Do we need high-order mutation in fault-based Boolean-specification testing?Journal of Systems and Software10.1016/j.jss.2023.111933210:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111933
Ahmed ZSchwass EHerbold STrautsch FGrabowski J(2024)A new perspective on the competent programmer hypothesis through the reproduction of real faults with repeated mutationsSoftware Testing, Verification and Reliability10.1002/stvr.1874Online publication date: 29-Feb-2024
https://doi.org/10.1002/stvr.1874
Mao RZhang LZhang X(2024)Mutation‐based data augmentation for software defect predictionJournal of Software: Evolution and Process10.1002/smr.263436:6Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1002/smr.2634
Laurent TArcaini PTrubiani CVentresque A(2022)Mutation-based analysis of queueing network performance modelsJournal of Systems and Software10.1016/j.jss.2022.111385191:COnline publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1016/j.jss.2022.111385
Rodríguez IRosa-Velardo FRubio F(2020)Introducing complexity to formal testingJournal of Logical and Algebraic Methods in Programming10.1016/j.jlamp.2019.100502111(100502)Online publication date: Feb-2020
https://doi.org/10.1016/j.jlamp.2019.100502
Hierons RGazda MGómez-Abajo PLefticaru RMerayo M(2020)Mutation Testing for RoboChartSoftware Engineering for Robotics10.1007/978-3-030-66494-7_11(345-375)Online publication date: 17-Dec-2020
https://doi.org/10.1007/978-3-030-66494-7_11
Sugumar GMathur A(2019)A Method for Testing Distributed Anomaly DetectorsInternational Journal of Critical Infrastructure Protection10.1016/j.ijcip.2019.100324(100324)Online publication date: Oct-2019
https://doi.org/10.1016/j.ijcip.2019.100324
Reichstaller AEberhardinger BPonsar HKnapp AReif WBai XLi JUlrich A(2018)Test suite reduction for self-organizing systemsProceedings of the 13th International Workshop on Automation of Software Test10.1145/3194733.3194739(64-70)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3194733.3194739
Ma LZhang FSun JXue MLi BJuefei-Xu FXie CLi LLiu YZhao JWang Y(2018)DeepMutation: Mutation Testing of Deep Learning Systems2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE.2018.00021(100-111)Online publication date: Oct-2018
https://doi.org/10.1109/ISSRE.2018.00021
Gopinath RMathis BZeller A(2018)If You Can't Kill a Supermutant, You Have a Problem2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW.2018.00023(18-24)Online publication date: Apr-2018
https://doi.org/10.1109/ICSTW.2018.00023
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