The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mining metrics to predict component failures
Abstract
What is it that makes software fail? In an empirical study of the post-release defect history of five Microsoft software systems, we found that failure-prone software entities are statistically correlated with code complexity measures. However, there is no single set of complexity metrics that could act as a universally best defect predictor. Using principal component analysis on the code metrics, we built regression models that accurately predict the likelihood of post-release defects for new entities. The approach can easily be generalized to arbitrary projects; in particular, predictors obtained from one project can also be significant for new, similar projects.
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Citations
Cites
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
Self-citation
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
Predicting defects using network analysis on dependency graphs
Zimmermann, T., & Nagappan, N. (2008) · Proceedings of the 13th international conference on Software engineering - ICSE '08 · doi:10.1145/1368088.1368161 · Cited by 580
Self-citationPredicting Faults from Cached History
Kim, S., Zimmermann, T., Whitehead Jr., E. J., & Zeller, A. (2007) · 29th International Conference on Software Engineering (ICSE'07) · doi:10.1109/icse.2007.66 · Cited by 486
Self-citationRevisiting 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
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2982385 · Cited by 148
Metadata
- Authors
- Nagappan, N., Ball, T., & Zeller, A.
- Venue
- Proceedings of the 28th international conference on Software engineering
- Publisher
- ACM
- Published
- 2006-05-28
- Pages
- 452-461
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2105300539
- Cited by
- 782
- References
- 1
- FWCI
- 32.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nagappan, N., Ball, T., & Zeller, A. (2006). Mining metrics to predict component failures. Proceedings of the 28th international conference on Software engineering. https://doi.org/10.1145/1134285.1134349
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