Fault centrality: boosting spectrum-based fault localization via local influence calculation

Zhao, Guyu; He, Hongdou; Huang, Yifang

doi:10.1007/s10489-021-02822-4

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…In order to analyze the impact relationship of faults, Vancsics et al [30] constructed a new method based on counting different function call contexts to analyze the propagation of program faults. In order to reduce the impact range of fault nodes in software networks, Zhao et al [31] constructed a directional call network, proposed their own influence propagation relationship calculation method, and analyzed the local centrality of fault nodes in software networks. This paper uses a software execution network to represent data flow behavior during program execution and identifies and locates fault program statements' positions by leveraging complex network analysis capabilities.…”

Section: Program Execution Networkmentioning

confidence: 99%

Fault Localization Using TrustRank Algorithm

Fan,

Wu,

Zhang

et al. 2023

Applied Sciences

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Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.

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Section: Program Execution Networkmentioning

confidence: 99%

Fault Localization Using TrustRank Algorithm

Fan,

Wu,

Zhang

et al. 2023

Applied Sciences

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“…Ghosh et al [17] used logistic mapping function to achieve chaotic sequence, which first calculated the suspiciousness score for each program statement and then assigned ranks according to that score. Considering that the interactive behaviors among software entities implied some fault patterns, Zhao et al [18] introduced the fault influence of interactive entities and developed a novel synthetical fault localization approach based on the software network. Wu et al [19] adopted OPTICS clustering to group failed test cases, the failed test cases in this cluster, with all passed test cases to locate a single-bug.…”

Section: Spectrum-based Fault Localization (Sbfl)mentioning

confidence: 99%

An Innovative Framework for Coincidental Correctness Impacting on Fault Localization

Cao

Sun

2022

Symmetry

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An important research aspect of Spectrum-Based Fault Localization (SBFL) is the influence factors of the effectiveness of suspiciousness formulas from the perspective of symmetry. Coincidental correctness is one of the most important factors impacting the effectiveness of suspiciousness formulas. The influence of fault localization by coincidental correctness has attracted a large amount of research in the perspective of empirical study; however, it can hardly be considered as sufficiently comprehensive when there are a large number of the symmetrical suspiciousness formulas. Therefore, we first develop an innovative theoretical framework with function derivation investigating suspiciousness formulas impacted by coincidental correctness. We define three types of relations between formulas affected by coincidental correctness: namely, improved type, invariant type and uncertain type. We investigated 30 suspiciousness formulas using this framework and group them into three categories. Furthermore, we conduct an empirical study to verify the effectiveness of SBFL affected by coincidental correctness on four relatively large C programs. We proved that coincidental correctness has a positive effect on 23 out of these 30 formulas, no effect on 5 of them, and the effect on the remaining 2 of them depend on certain conditions. The experimental results show that the effectiveness of some suspiciousness formulas can be enhanced and that of some suspiciousness formulas remain unchanged.

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“…Several studies have been presented that use (call context) information extracted from static and/or dynamic call graphs [141,48,143] or program slices [75,142,133,132] in fault localization algorithms. By weighting the nodes of call graphs using some algorithm (e.g.…”

Section: Unique Count-based Spectramentioning

confidence: 99%

New Algorithms and Benchmarks for Supporting Spectrum-Based Fault Localization

Vancsics

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I never thought I would get this far... Until I started working as a BSc student at the Department of Software Engineering, I did not think about choosing a research career. I was always interested in new things and attracted by the joy of discovery, but a scientific career was a mystical world that seemed unattainable to me. Then, as I became more and more involved in research since 2012, it seemed that this is what I wanted to do. I liked that we examined a specific problem together from several aspects, tried to find a solution and if we succeeded, the team was happy with the result achieved. Of course, there were ups and downs, periods when the results did not come as we wanted, but there was always something (a new idea, a new challenge) or someone (one of my colleagues or my family) who pushed me through these stages.Complementing and motivating each other -I think I can say that we have achieved great success... Two people have influenced my research career so far the most: Tibor Gyimóthy and Árpád Beszédes. I would like to thank Tibor for offering me the opportunity to join the department's team. In the last lecture of the semester, he asked me if I would like to work for them. I really liked the idea, there was no question on my part whether I should go for it. This was the point where my thesis really began... I am grateful to Árpád for constantly being my mentor over the past decade. Without his professional and human support, this thesis would certainly not have been possible. I learned a lot (and am still learning) from him, for which I am very grateful.I thank the team for welcoming me so kindly and for being so patient and helpful when I joined them. I would also like to thank Tamás Gergely, Ferenc Horváth, Gergő Balogh, and Attila Szatmári, with whom I have worked for years and am still working. They are not only excellent colleagues but also excellent researchers who have continuously supported me during the research of the past years. Thanks for.And, of course, I thank my family and my girlfriend for supporting me and trusting me all the way, for creating the conditions for me to focus only on my studies. I can not thank them enough for that! THANK YOU!

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Fault centrality: boosting spectrum-based fault localization via local influence calculation

Cited by 7 publications

References 51 publications

Fault Localization Using TrustRank Algorithm

Fault Localization Using TrustRank Algorithm

An Innovative Framework for Coincidental Correctness Impacting on Fault Localization

New Algorithms and Benchmarks for Supporting Spectrum-Based Fault Localization

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