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Metallaxis‐FL: mutation‐based fault localization
Open accessAbstract
Summary: Fault localization methods seek to identify faulty program statements based on the information provided by the failing and passing test executions. Spectrum‐based methods are among the most popular ones and assist programmers by assigning suspiciousness values on program statements according to their probability of being faulty. This paper proposes Metallaxis, a fault localization approach based on mutation analysis. The innovative part of Metallaxis is that it uses mutants and links them with the faulty program places. Thus, mutants that are killed mostly by failing tests provide a good indication about the location of a fault. Experimentation using Metallaxis suggests that it is significantly more effective than statement‐based approaches. This is true even in the case where mutation cost‐reduction techniques, such as mutant sampling, are facilitated. Additionally, results from a controlled experiment show that the use of mutation as a testing technique provides benefits to the fault localization process. Therefore, fault localization is significantly improved by using mutation‐based tests instead of block‐based or branch‐based test suites. Finally, evidence in support of the methods’ scalability is also given. Copyright © 2013 John Wiley & Sons, Ltd.
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Cited by
Evaluating 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
Mutation Testing Advances: An Analysis and Survey
(2019) · Advances in Computers · doi:10.1016/bs.adcom.2018.03.015 · Cited by 425
An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
Qi, Z., Long, F., Achour, S., & Rinard, M. (2015) · Proceedings of the 2015 International Symposium on Software Testing and Analysis · doi:10.1145/2771783.2771791 · Cited by 420
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
Less training, more repairing please: revisiting automated program repair via zero-shot learning
Xia, C. S., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549101 · Cited by 231
Metadata
- Authors
- Papadakis, M., & Le Traon, Y.
- Venue
- Software Testing, Verification and Reliability
- Publisher
- Wiley
- Published
- 2013-09-09
- Volume
- 25
- Issue
- 5-7
- Pages
- 605-628
- ISSN
- 0960-0833 (ISSN-L)
- 1099-1689
- OpenAlex
- W1950030762
- Cited by
- 371
- References
- 0
- FWCI
- 21.53
- Type
- journal article
- Language
- en
- Open access
- bronze
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