On the analysis of spectrum based fault localization using hitting sets

Tu, Jingxuan; Xie, Xue‐Jun; Chen, Tsong Yueh; Xu, Baowen

doi:10.1016/j.jss.2018.10.013

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…This method dynamically weights nodes in the program control ŕow graph based on test case execution to more accurately pinpoint faulty program statements. Tu, Jingxuan et al (Tu, Xie, Chen, & Xu, 2019) conducted theoretical analysis of SENDYS and proposed an improved version called SENDYS+, which achieves enhanced effectiveness. PengZhendong et al (Peng et al, 2020) presented ABFL, a bug localization method based on an autoencoder.…”

Section: Related Work Of Combined Bug Localizationmentioning

confidence: 99%

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Guo,

Ji,

et al. 2024

Preprint

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Regression testing aims to confirm that program changes don't disrupt existing functionalities. Automated fault localization improves quality and efficiency of regression testing. Spectrum-Based Fault Localization (SBFL) is adept at identifying faults in program statements using test case execution data. However, SBFL overlooks faults due to structural anomalies and can't detect non-existent or redundant statements. This study introduces Program Structure Check (PSC) to address the issue. In regression testing, historical program versions provide valuable information for fault localization. PSC compares the structure of the program being tested with programs of historical versions to find structural differences, like missing code. This increases suspicion scores at these locations. Experimental findings show PSC detects over 90% of structural bugs, with over 76% ranked highest on the suspicion list. We combine PSC with SBFL, termed PSC-SBFL, and test it on public assemblies to assess bug location effects. Results indicate that adding PSC to SBFL enhances bug ranking by approximately 93% and reduces manual code checking by about 34% when all bugs are identified. Compared to another SBFL-based method, PSC-SBFL demonstrates superior bug localization. These findings underscore how combining PSC and SBFL algorithms enhances bug localization accuracy, expedites bug identification, and boosts software quality.

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Section: Related Work Of Combined Bug Localizationmentioning

confidence: 99%

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Guo,

Ji,

et al. 2024

Preprint

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“…We will compare the effectiveness of the two approaches in two scenarios: using the test case resampling approach and without using it. Furthermore, we choose those widely used large‐sized programs in the field of software debugging (e.g., previous studies 1,2,4‐7 ) for the experiments, varying from 5 KLOC to 491 KLOC.…”

Section: An Experimental Studymentioning

confidence: 99%

“…It usually needs much manual involvement and has been proved to be one of the most expensive and time-consuming activities for software developers. In order to reduce the cost, researchers have developed many fault localization techniques to provide the assistance in seeking the positions of the faults in the program (e.g., previous studies [1][2][3][4][5][6][7] ). The recent progress on deep learning shows its promising ability of learning useful models in various applications (e.g., image classification, object detection, and segmentation) and providing tremendous improvement in robustness and accuracy.8Some researchers have exploited the use of this learning ability to discuss and evaluate the potential of deep learning in fault localization.…”

Section: Introductionmentioning

confidence: 99%

Improving deep‐learning‐based fault localization with resampling

Zhang

Lei

Mao

et al. 2020

J Software Evolu Process

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Many fault localization approaches recently utilize deep learning to learn an effective localization model showing a fresh perspective with promising results. However, localization models are generally learned from class imbalance datasets; that is, the number of failing test cases is much fewer than passing test cases. It may be highly susceptible to affect the accuracy of learned localization models. Thus, in this paper, we explore using data resampling to reduce the negative effect of the imbalanced class problem and improve the accuracy of learned models of deep‐learning‐based fault localization. Specifically, for deep‐learning‐based fault localization, its learning feature may require duplicate essential data to enhance the weak but beneficial experience incurred by the class imbalance datasets. We leverage the property of test cases (i.e., passing or failing) to identify failing test cases as the duplicate essential data and propose an iterative oversampling approach to resample failing test cases for producing a class balanced test suite. We apply the test case resampling to representative localization models using deep learning. Our empirical results on eight large‐sized programs with real faults and four large‐sized programs with seeded faults show that the test case resampling significantly improves fault localization effectiveness.

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“…We utilized SBFL techniques to drive our target labels. There is a plethora of studies on SBFL techniques in the literature (e.g., [196,31,24,174,178]). De Souza et al in [32] and Pearson et al in [141] reviewed and evaluated the most frequently used techniques.…”

Section: Predicting the Quality Of Fault Localizationmentioning

confidence: 99%

A Framework for Failure Diagnosis

Golagha

2017

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

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Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not have been possible to do without the support and guidance that I received from many people, and I would not forget to thank them. My Supervisor: Alex, you decided to trust your instinct. Your decision changed a lot of things in my life. I worked hard to prove your instinct was right. Throughout this journey, under your supervision, I grew and became the woman I am today. Thank you for helping me understanding my strengths and weaknesses, teaching me how to accept them and how to use them. Thank you for showing me how to admit and say sorry when I am wrong, how to argue and convince when I am right, and how to confidently say "I don't know but I'll find out". I learned more than you know from you. I am more than grateful. My second supervisor: Lionel, I would like to express my sincere gratitude. Thank you for hosting me at SnT center and providing useful and actionable feedback. I was inspired by many of your previous works. My short stay with your research group and interactions with colleagues there provided me new insight.

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On the analysis of spectrum based fault localization using hitting sets

Cited by 15 publications

References 30 publications

PSC-SBFL: combining PSC with SBFL to improve software fault localization

PSC-SBFL: combining PSC with SBFL to improve software fault localization

Improving deep‐learning‐based fault localization with resampling

A Framework for Failure Diagnosis

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