Article

A similarity-aware approach to testing based fault localization

Authors:

Jiasu SunAuthors Info & Claims

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

Pages 291 - 294

https://doi.org/10.1145/1101908.1101953

Published: 07 November 2005 Publication History

Abstract

Debugging is a time-consuming task in software development and maintenance. To accelerate this task, several approaches have been proposed to automate fault localization. In particular, testing based fault localization (TBFL), which utilizes the testing information to localize the faults, seem to be very promising. However, the similarity between test cases in the test suite has been ignored in the research on TBFL. In this paper, we investigate this similarity issue and propose a novel approach named similarity-aware fault localization (SAFL), which can calculate the suspicion probability of each statement with little impact by the similarity issue. To address and deal with the similarity between test cases, SAFL applies the theory of fuzzy sets to remove the uneven distribution of the test cases. We also performed an experimental study for two real-world programs at different size levels to evaluate SAFL together with another two approaches to TBFL. Experimental results show that SAFL is more effective than the other two approaches when the test suites contain injected redundancy, and SAFL can achieve a competitive result with normal test suites. SAFL can also be more effective than applying test suite reduction to current approaches to TBFL.

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Cited By

Elgendy IHierons RMcMinn P(2025)A Systematic Mapping Study of the Metrics, Uses and Subjects of Diversity‐Based Testing TechniquesSoftware Testing, Verification and Reliability10.1002/stvr.191435:2Online publication date: 17-Jan-2025
https://doi.org/10.1002/stvr.1914
Hu JLei Y(2024)A deep semantics-aware data augmentation method for fault localizationInformation and Software Technology10.1016/j.infsof.2024.107409168(107409)Online publication date: Apr-2024
https://doi.org/10.1016/j.infsof.2024.107409
Lei YWen TXie HFu LLiu CXu LSun H(2023)Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00045(304-315)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00045
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A similarity-aware approach to testing based fault localization

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cover image ACM Conferences

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

November 2005

482 pages

ISBN:1581139934

DOI:10.1145/1101908

General Chair:
David Redmiles
University of California, Irvine, CA
,
Program Chairs:
Tom Ellman
Vassar College
,
Andrea Zisman
City University, UK

Copyright © 2005 ACM.

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Publication History

Published: 07 November 2005

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ASE05: International Conference on Automated Software Engineering 2005

November 7 - 11, 2005

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Elgendy IHierons RMcMinn P(2025)A Systematic Mapping Study of the Metrics, Uses and Subjects of Diversity‐Based Testing TechniquesSoftware Testing, Verification and Reliability10.1002/stvr.191435:2Online publication date: 17-Jan-2025
https://doi.org/10.1002/stvr.1914
Hu JLei Y(2024)A deep semantics-aware data augmentation method for fault localizationInformation and Software Technology10.1016/j.infsof.2024.107409168(107409)Online publication date: Apr-2024
https://doi.org/10.1016/j.infsof.2024.107409
Lei YWen TXie HFu LLiu CXu LSun H(2023)Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00045(304-315)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00045
Cao HWang FDeng MWang XChu Y(2023)Software multiple-fault localization using particle swarm optimization via genetic operationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.02.02335:4(21-35)Online publication date: Apr-2023
https://doi.org/10.1016/j.jksuci.2023.02.023
Zhang ZLi YYang SLei Y(2023)Model-domain failing test augmentation with Generative Adversarial NetworksExpert Systems with Applications10.1016/j.eswa.2023.121901(121901)Online publication date: Oct-2023
https://doi.org/10.1016/j.eswa.2023.121901
Zhang ZXue JYang DMao X(2023)ContextAug: model-domain failing test augmentation with contextual informationFrontiers of Computer Science10.1007/s11704-023-2521-218:2Online publication date: 13-Sep-2023
https://doi.org/10.1007/s11704-023-2521-2
Zhang ZLei YMao XYan MXia X(2022)Improving Fault Localization Using Model-domain Synthesized Failing Test Generation2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME55016.2022.00026(199-210)Online publication date: Oct-2022
https://doi.org/10.1109/ICSME55016.2022.00026
Zou DLiang JXiong YErnst MZhang L(2021)An Empirical Study of Fault Localization Families and Their CombinationsIEEE Transactions on Software Engineering10.1109/TSE.2019.289210247:2(332-347)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1109/TSE.2019.2892102
Zhang ZLei YMao XYan MXu LWen J(2021)Improving deep‐learning‐based fault localization with resamplingJournal of Software: Evolution and Process10.1002/smr.231233:3Online publication date: 3-Mar-2021
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Johnson BBrun YMeliou ARothermel GBae D(2020)Causal testingProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380377(87-99)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380377
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