The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Practitioners' expectations on automated fault localization
Open accessAbstract
Software engineering practitioners often spend significant amount of time and effort to debug. To help practitioners perform this crucial task, hundreds of papers have proposed various fault localization techniques. Fault localization helps practitioners to find the location of a defect given its symptoms (e.g., program failures). These localization techniques have pinpointed the locations of bugs of various systems of diverse sizes, with varying degrees of success, and for various usage scenarios. Unfortunately, it is unclear whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by surveying 386 practitioners from more than 30 countries across 5 continents about their expectations of research in fault localization. In particular, we investigated a number of factors that impact practitioners' willingness to adopt a fault localization technique. We then compared what practitioners need and the current state-of-research by performing a literature review of papers on fault localization techniques published in ICSE, FSE, ESEC-FSE, ISSTA, TSE, and TOSEM in the last 5 years (2011-2015). From this comparison, we highlight the directions where researchers need to put effort to develop fault localization techniques that matter to practitioners.
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Cites
The proof and measurement of association between two things
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412159 · Cited by 8,039
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
GZoltar: an eclipse plug-in for testing and debugging
Campos, J., Riboira, A., Perez, A., & Abreu, R. (2012) · Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/2351676.2351752 · Cited by 208
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
Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
Kang, S., Yoon, J., & Yoo, S. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00194 · Cited by 204
Yu, G., Chen, P., Chen, H., Guan, Z., Huang, Z., Jing, L., Weng, T., Sun, X., & Li, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449905 · Cited by 158
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
Kang, S., An, G., & Yoo, S. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660771 · Cited by 128
Accessibility in Software Practice: A Practitioner’s Perspective
Bi, T., Xia, X., Lo, D., Grundy, J., Zimmermann, T., & Ford, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3503508 · Cited by 109
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Metadata
- Authors
- Kochhar, P. S., Xia, X., Lo, D., & Li, S.
- Venue
- Proceedings of the 25th International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2016-07-18
- Pages
- 165-176
- OpenAlex
- W2467903332
- Cited by
- 346
- References
- 3
- FWCI
- 18.07
- Type
- conference paper
- Language
- en
- Open access
- green
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