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Empirical evaluation of the tarantula automatic fault-localization technique
Abstract
The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study---it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.
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Citations
Cited by
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
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
Zhou, J., Zhang, H., & Lo, D. (2012) · 2012 34th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2012.6227210 · Cited by 507
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
Large Language Models for Test-Free Fault Localization
Yang, A. Z. H., Le Goues, C., Martins, R., & Hellendoorn, V. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3623342 · Cited by 144
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
“Dynamic FL techniques mainly include spectrum-based FL (SBFL) [Abreu et al. 2009, 2007; Jones and Harrold 2005] and mutation-based FL (MBFL) [Lou et al. 2020; Moon et al. 2014; Papadakis and Le Traon 2015].”
Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset
Martinez, M., Durieux, T., Xuan, J., Sommerard, R., & Monperrus, M. (2015) · arXiv · doi:10.48550/arxiv.1505.07002 · Cited by 47
Yu, G., Huang, Z., & Chen, P. (2023) · Journal of Software: Evolution and Process · doi:10.1002/smr.2413 · Cited by 44
Using entropy measures for comparison of software traces
Miranskyy, A. V., Davison, M., Reesor, R. M., & Murtaza, S. S. (2012) · Information Sciences · doi:10.1016/j.ins.2012.03.017 · Cited by 27
Agentless: Demystifying LLM-based Software Engineering Agents
Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2024) · arXiv · doi:10.48550/arxiv.2407.01489 · Cited by 24
“Dynamic FL techniques mainly include spectrum-based FL (SBFL) [47, 25, 24] and mutation-based FL (MBFL) [77, 68, 63].”
MethodPre-training Code Representation with Semantic Flow Graph for Effective Bug Localization
Du, Y., & Yu, Z. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616338 · Cited by 19
T-Rank:A Lightweight Spectrum based Fault Localization Approach for Microservice Systems
Ye, Z., Chen, P., & Yu, G. (2021) · 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · doi:10.1109/ccgrid51090.2021.00051 · Cited by 19
Entropy Guided Spectrum Based Bug Localization Using Statistical Language Model
Chakraborty, S., Li, Y., Irvine, M., Saha, R., & Ray, B. (2018) · arXiv · doi:10.48550/arxiv.1802.06947 · Cited by 7
“Existing bug localization techniques can be broadly classified into two categories: i) test coverage-based dynamic approaches [30, 2, 59, 14, 37, 38], and ii) pattern-based [15, 19, 18, 13] or information retrieval-based (IR) static approaches [44, 60, 48, 58].”
BackgroundMethod
Metadata
- Authors
- Jones, J. A., & Harrold, M. J.
- Venue
- Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering
- Publisher
- ACM
- Published
- 2005-11-07
- Pages
- 273-282
- OpenAlex
- W2101819268
- Cited by
- 1,238
- References
- 0
- FWCI
- 10.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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