The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
FLUCCS: using code and change metrics to improve fault localization
Abstract
Fault localization aims to support the debugging activities of human developers by highlighting the program elements that are suspected to be responsible for the observed failure. Spectrum Based Fault Localization (SBFL), an existing localization technique that only relies on the coverage and pass/fail results of executed test cases, has been widely studied but also criticized for the lack of precision and limited effort reduction. To overcome restrictions of techniques based purely on coverage, we extend SBFL with code and change metrics that have been studied in the context of defect prediction, such as size, age and code churn. Using suspiciousness values from existing SBFL formulas and these source code metrics as features, we apply two learn-to-rank techniques, Genetic Programming (GP) and linear rank Support Vector Machines (SVMs). We evaluate our approach with a ten-fold cross validation of method level fault localization, using 210 real world faults from the Defects4J repository. GP with additional source code metrics ranks the faulty method at the top for 106 faults, and within the top five for 173 faults. This is a significant improvement over the state-of-the-art SBFL formulas, the best of which can rank 49 and 127 faults at the top and within the top five, respectively.
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Cites
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Defects4J: a database of existing faults to enable controlled testing studies for Java programs
Just, R., Jalali, D., & Ernst, M. D. (2014) · Proceedings of the 2014 International Symposium on Software Testing and Analysis · doi:10.1145/2610384.2628055 · Cited by 1,447
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
Use of relative code churn measures to predict system defect density
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Cited by
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization
Li, X., Li, W., Zhang, Y., & Zhang, L. (2019) · Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3293882.3330574 · Cited by 336
Boosting coverage-based fault localization via graph-based representation learning
Lou, Y., Zhu, Q., Dong, J., Li, X., Sun, Z., Hao, D., Zhang, L., & Zhang, L. (2021) · Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3468264.3468580 · Cited by 163
Widyasari, R., Sim, S. Q., Lok, C., Qi, H., Phan, J., Tay, Q., Tan, C., Wee, F., Tan, J. E., Yieh, Y., et al. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3417943 · Cited by 150
Demystifying LLM-Based Software Engineering Agents
Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3715754 · Cited by 849
“Moreover, learning-based techniques have also been proposed to leverage machine learning to combine multiple sources of dynamic/static information, including DeepFL [Li et al. 2019], FLUCCS [Sohn and Yoo 2017], and TRANSFER [Meng et al. 2022].”
Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation
Rafi, M. N., Kim, D. J., Chen, A. R., Chen, T.-H. (., & Wang, S. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660793 · Cited by 24
Metadata
- Authors
- Sohn, J., & Yoo, S.
- Venue
- Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2017-07-10
- Pages
- 273-283
- OpenAlex
- W2736091366
- Cited by
- 242
- References
- 6
- FWCI
- 11.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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