The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Spectrum-Based Multiple Fault Localization
Abstract
Fault diagnosis approaches can generally be categorized into spectrum-based fault localization (SFL, correlating failures with abstractions of program traces), and model-based diagnosis (MBD, logic reasoning over a behavioral model). Although MBD approaches are inherently more accurate than SFL, their high computational complexity prohibits application to large programs. We present a framework to combine the best of both worlds, coined BARINEL. The program is modeled using abstractions of program traces (as in SFL) while Bayesian reasoning is used to deduce multiple-fault candidates and their probabilities (as in MBD). A particular feature of BARINEL is the usage of a probabilistic component model that accounts for the fact that faulty components may fail intermittently. Experimental results on both synthetic and real software programs show that BARINEL typically outperforms current SFL approaches at a cost complexity that is only marginally higher. In the context of single faults this superiority is established by formal proof.
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Cites
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Visualization of test information to assist fault localization
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On the Accuracy of Spectrum-based Fault Localization
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Self-citationScalable statistical bug isolation
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Cited by
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
Self-citationEvaluating and Improving Fault Localization
Pearson, S., Campos, J., Just, R., Fraser, G., Abreu, R., Ernst, M. D., Pang, D., & Keller, B. (2017) · 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2017.62 · Cited by 426
Self-citationZhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Li, W., & Ding, D. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2887384 · Cited by 410
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
A survey of AIOps methods for failure management
Notaro, P., Cardoso, J., & Gerndt, M. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3483424 · Cited by 142
Metadata
- Authors
- Abreu, R., Zoeteweij, P., & van Gemund, A. J. C.
- Venue
- 2009 IEEE/ACM International Conference on Automated Software Engineering
- Publisher
- IEEE
- Published
- 2009-11
- Pages
- 88-99
- OpenAlex
- W2172154252
- Cited by
- 401
- References
- 35
- FWCI
- 13.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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