The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
DynaMine: finding common error patterns by mining software revision histories
Abstract
A great deal of attention has lately been given to addressing software bugs such as errors in operating system drivers or security bugs. However, there are many other lesser known errors specific to individual applications or APIs and these violations of application-specific coding rules are responsible for a multitude of errors. In this paper we propose DynaMine, a tool that analyzes source code check-ins to find highly correlated method calls as well as common bug fixes in order to automatically discover application-specific coding patterns. Potential patterns discovered through mining are passed to a dynamic analysis tool for validation; finally, the results of dynamic analysis are presented to the user.The combination of revision history mining and dynamic analysis techniques leveraged in DynaMine proves effective for both discovering new application-specific patterns and for finding errors when applied to very large applications with many man-years of development and debugging effort behind them. We have analyzed Eclipse and jEdit, two widely-used, mature, highly extensible applications consisting of more than 3,600,000 lines of code combined. By mining revision histories, we have discovered 56 previously unknown, highly application-specific patterns. Out of these, 21 were dynamically confirmed as very likely valid patterns and a total of 263 pattern violations were found.
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Cites
A static analyzer for large safety-critical software
Blanchet, B., Cousot, P., Cousot, R., Feret, J., Mauborgne, L., Miné, A., Monniaux, D., & Rival, X. (2003) · Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation · doi:10.1145/781131.781153 · Cited by 330
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GPLAG: detection of software plagiarism by program dependence graph analysis
Liu, C., Chen, C., Han, J., & Yu, P. S. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150522 · Cited by 422
On the naturalness of software
Hindle, A., Barr, E. T., Gabel, M., Su, Z., & Devanbu, P. (2016) · Communications of the ACM · doi:10.1145/2902362 · Cited by 307
Early Detection of Collaboration Conflicts and Risks
Brun, Y., Holmes, R., Ernst, M. D., & Notkin, D. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2013.28 · Cited by 82
TwinMem-Agent: Episodic and Semantic Memory for Repository-Level Program Repair
Wu, B., Luo, Z., Mu, F., & Xiang, L. (2026) · Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3803437.3807664
Metadata
- Authors
- Livshits, B., & Zimmermann, T.
- Venue
- Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
- Publisher
- ACM
- Published
- 2005-09
- Pages
- 296-305
- OpenAlex
- W2124666592
- Cited by
- 330
- References
- 1
- FWCI
- 20.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
