The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Elixir: Effective object-oriented program repair
Open accessAbstract
This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.
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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
Zhou, J., Zhang, H., & Lo, D. (2012) · 2012 34th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2012.6227210 · Cited by 507
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
The ManyBugs and IntroClass benchmarks for automated repair of C programs
Le Goues, C., Holtschulte, N., Smith, E. K., Brun, Y., Devanbu, P., Forrest, S., & Weimer, W. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2015.2454513 · Cited by 326 · cited 1×
“To investigate this empirically, we selected three Java projects: Eclipse JDT, Platform, and BIRT, which are popular Java projects used in bug localization research [22] and the ManyBugs [25] benchmarks, widely used in C program repair research, representing C applications.”
Method
Cited by
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
Chen, Z., Kommrusch, S. J., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., & Monperrus, M. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2940179 · Cited by 396
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
Impact of Code Language Models on Automated Program Repair
Jiang, N., Liu, K., Lutellier, T., & Tan, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00125 · Cited by 202
Can OpenAI's codex fix bugs?: an evaluation on QuixBugs
Prenner, J. A., Babii, H., & Robbes, R. (2022) · Proceedings of the Third International Workshop on Automated Program Repair · doi:10.1145/3524459.3527351 · Cited by 99
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
Metadata
- Authors
- Saha, R. K., Lyu, Y., Yoshida, H., & Prasad, M. R.
- Venue
- 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 648-659
- OpenAlex
- W2767766265
- Cited by
- 189
- References
- 4
- FWCI
- 14.73
- Type
- conference paper
- Language
- en
- Open access
- green
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- Saha, R. K., Lyu, Y., Yoshida, H., & Prasad, M. R. (2017). Elixir: Effective object-oriented program repair. 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). https://doi.org/10.1109/ase.2017.8115675
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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