research-article

A universal data augmentation approach for fault localization

Authors:

Xiaoguang MaoAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 48 - 60

https://doi.org/10.1145/3510003.3510136

Published: 05 July 2022 Publication History

Abstract

Data is the fuel to models, and it is still applicable in fault localization (FL). Many existing elaborate FL techniques take the code coverage matrix and failure vector as inputs, expecting the techniques could find the correlation between program entities and failures. However, the input data is high-dimensional and extremely unbalanced since the real-world programs are large in size and the number of failing test cases is much less than that of passing test cases, which are posing severe threats to the effectiveness of FL techniques.

To overcome the limitations, we propose Aeneas, a universal data augmentation approach that gener<u>A</u>t<u>e</u>s sy<u>n</u>thesized failing t<u>e</u>st cases from reduced fe<u>a</u>ture <u>s</u>pace for more precise fault localization. Specifically, to improve the effectiveness of data augmentation, Aeneas applies a revised principal component analysis (PCA) first to generate reduced feature space for more concise representation of the original coverage matrix, which could also gain efficiency for data synthesis. Then, Aeneas handles the imbalanced data issue through generating synthesized failing test cases from the reduced feature space through conditional variational autoencoder (CVAE). To evaluate the effectiveness of Aeneas, we conduct large-scale experiments on 458 versions of 10 programs (from ManyBugs, SIR, and Defects4J) by six state-of-the-art FL techniques. The experimental results clearly show that Aeneas is statistically more effective than baselines, e.g., our approach can improve the six original methods by 89% on average under the Top-1 accuracy.

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Index Terms

A universal data augmentation approach for fault localization
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 Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

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Published: 05 July 2022

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the National Natural Science Foundation of China
the Fundamental Research Funds for the Central Universities
the Natural Science Foundation of Chongqing
the National Key Research and Development Project of China
the National Defense Basic Scientific Research Project

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ICSE '22

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SIGSOFT

ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

Pennsylvania, Pittsburgh

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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https://doi.org/10.1016/j.measurement.2024.116589
Hu J(2025)CG-FL: A data augmentation approach using context-aware genetic algorithm for fault localizationJournal of Systems and Software10.1016/j.jss.2025.112359222(112359)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112359
ZHANG ZLI DXIA LLI YMENG X(2024)A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAEIEICE Transactions on Information and Systems10.1587/transinf.2023EDL8052E107.D:2(234-238)Online publication date: 1-Feb-2024
https://doi.org/10.1587/transinf.2023EDL8052
Xie HLei YLi MYan MZhang SFilkov VRay BZhou M(2024)Combining Coverage and Expert Features with Semantic Representation for Coincidental Correctness DetectionProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695542(1770-1782)Online publication date: 27-Oct-2024
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Jiang SZhang JChen WWang BZhou JZhang J(2024)Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMsProceedings of the 1st International Workshop on Large Language Models for Code10.1145/3643795.3648390(75-78)Online publication date: 20-Apr-2024
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Csuvik VAszmann RBeszédes ÁHorváth FGyimóthy T(2024)On the Stability and Applicability of Deep Learning in Fault Localization2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00062(546-555)Online publication date: 12-Mar-2024
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