The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations
Open accessAbstract
Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.
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Cites
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
Shaping program repair space with existing patches and similar code
Jiang, J., Xiong, Y., Zhang, H., Gao, Q., & Chen, X. (2018) · Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3213846.3213871 · Cited by 329
Is the cure worse than the disease? overfitting in automated program repair
Smith, E. K., Barr, E. T., Le Goues, C., & Brun, Y. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786825 · Cited by 323
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
Automated Program Repair in the Era of Large Pre-trained Language Models
Xia, C. S., Wei, Y., & Zhang, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00129 · Cited by 433
Automated Repair of Programs from Large Language Models
Fan, Z., Gao, X., Mirchev, M., Roychoudhury, A., & Tan, S. H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00128 · Cited by 256
A critical review on the evaluation of automated program repair systems
Liu, K., Li, L., Koyuncu, A., Kim, D., Liu, Z., Klein, J., & Bissyandé, T. F. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2020.110817 · Cited by 79
Self-citationThinkRepair: Self-Directed Automated Program Repair
Yin, X., Ni, C., Wang, S., Li, Z., Zeng, L., & Yang, X. (2024) · Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3650212.3680359 · Cited by 68
The Plastic Surgery Hypothesis in the Era of Large Language Models
Xia, C. S., Ding, Y., & Zhang, L. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00047 · Cited by 67
Metadata
- Authors
- Liu, K., Koyuncu, A., Kim, D., & Bissyande, T. F.
- Venue
- 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 1-12
- OpenAlex
- W2904214673
- Cited by
- 171
- References
- 4
- FWCI
- 14.54
- Type
- conference paper
- Language
- en
- Open access
- green
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- Liu, K., Koyuncu, A., Kim, D., & Bissyande, T. F. (2019). AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations. 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). https://doi.org/10.1109/saner.2019.8667970
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