research-article

Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs

Authors:

Moonzoo KimAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 28, Issue 4

Article No.: 23, Pages 1 - 34

https://doi.org/10.1145/3345628

Published: 09 October 2019 Publication History

Abstract

Finding the root cause of a bug requires a significant effort from developers. Automated fault localization techniques seek to reduce this cost by computing the suspiciousness scores (i.e., the likelihood of program entities being faulty). Existing techniques have been developed by utilizing input features of specific types for the computation of suspiciousness scores, such as program spectrum or mutation analysis results. This article presents a novel learn-to-rank fault localization technique called PRecise machINe-learning-based fault loCalization tEchnique (PRINCE). PRINCE uses genetic programming (GP) to combine multiple sets of localization input features that have been studied separately until now. For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques. It also uses static features, such as dependency information and structural complexity of program entities. All such information is used by GP to train a ranking model for fault localization. The empirical evaluation on 65 real-world faults from CoREBench, 84 artificial faults from SIR, and 310 real-world faults from Defects4J shows that PRINCE outperforms the state-of-the-art SBFL, MBFL, and learn-to-rank techniques significantly. PRINCE localizes a fault after reviewing 2.4% of the executed statements on average (4.2 and 3.0 times more precise than the best of the compared SBFL and MBFL techniques, respectively). Also, PRINCE ranks 52.9% of the target faults within the top ten suspicious statements.

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Cited By

Zhang XSong YXie XXin QXing CFilkov VRay BZhou M(2024)Do not neglect what's on your hands: localizing software faults with exception trigger streamProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695479(982-994)Online publication date: 27-Oct-2024
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Wang HYang KWu T(2024)Optimizing Mutation-Based Fault Localization Through Contribution-Based Test Case ReductionInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450027X34:10(1537-1564)Online publication date: 5-Jul-2024
https://doi.org/10.1142/S021819402450027X
Wang RFan MYan YJiang S(2024)Combining Error Guessing and Logical Reasoning for Software Fault Localization via Deep LearningInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450021934:09(1485-1511)Online publication date: 28-May-2024
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Index Terms

Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs
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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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 28, Issue 4

October 2019

231 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3360049

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Published: 09 October 2019

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 July 2018

Published in TOSEM Volume 28, Issue 4

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https://dl.acm.org/doi/10.1145/3691620.3695479
Wang HYang KWu T(2024)Optimizing Mutation-Based Fault Localization Through Contribution-Based Test Case ReductionInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450027X34:10(1537-1564)Online publication date: 5-Jul-2024
https://doi.org/10.1142/S021819402450027X
Wang RFan MYan YJiang S(2024)Combining Error Guessing and Logical Reasoning for Software Fault Localization via Deep LearningInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450021934:09(1485-1511)Online publication date: 28-May-2024
https://doi.org/10.1142/S0218194024500219
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