The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Predicting faults using the complexity of code changes
Abstract
Predicting the incidence of faults in code has been commonly associated with measuring complexity. In this paper, we propose complexity metrics that are based on the code change process instead of on the code. We conjecture that a complex code change process negatively affects its product, i.e., the software system. We validate our hypothesis empirically through a case study using data derived from the change history for six large open source projects. Our case study shows that our change complexity metrics are better predictors of fault potential in comparison to other well-known historical predictors of faults, i.e., prior modifications and prior faults.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
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
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.91 · Cited by 364
Self-citationExamining the Impact of Self-Admitted Technical Debt on Software Quality
Wehaibi, S., Shihab, E., & Guerrouj, L. (2016) · 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) · doi:10.1109/saner.2016.72 · Cited by 114
Social engineering in social networking sites: Affect-based model
Algarni, A., Xu, Y., Chan, T., & Tian, Y.-C. (2013) · 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013) · doi:10.1109/icitst.2013.6750253 · Cited by 41
On the Relationship between Program Evolution and Fault-Proneness: An Empirical Study
Jaafar, F., Hassaine, S., Gueheneuc, Y., Hamel, S., & Adams, B. (2013) · 2013 17th European Conference on Software Maintenance and Reengineering · doi:10.1109/csmr.2013.12 · Cited by 5
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Deng, X., Da, J., Pan, E., He, Y. Y., Ide, C., Garg, K., Lauffer, N., Park, A., Pasari, N., Rane, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.16941 · Cited by 4
“In contrast, industrial software engineering, particularly in enterprise settings, often demands multi-file modifications spanning hundreds of lines [9, 16].”
Metadata
- Authors
- Hassan, A. E.
- Venue
- 2009 IEEE 31st International Conference on Software Engineering
- Publisher
- IEEE
- Published
- 2009
- Pages
- 78-88
- OpenAlex
- W2158744032
- Cited by
- 708
- References
- 2
- FWCI
- 23.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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