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Revisiting common bug prediction findings using effort-aware models
Abstract
Bug prediction models are often used to help allocate software quality assurance efforts (e.g. testing and code reviews). Mende and Koschke have recently proposed bug prediction models that are effort-aware. These models factor in the effort needed to review or test code when evaluating the effectiveness of prediction models, leading to more realistic performance evaluations. In this paper, we revisit two common findings in the bug prediction literature: 1) Process metrics (e.g., change history) outperform product metrics (e.g., LOC), 2) Package-level predictions outperform file-level predictions. Through a case study on three projects from the Eclipse Foundation, we find that the first finding holds when effort is considered, while the second finding does not hold. These findings validate the practical significance of prior findings in the bug prediction literature and encourage their adoption in practice.
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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
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
Deep Learning for Just-in-Time Defect Prediction
Yang, X., Lo, D., Xia, X., Zhang, Y., & Sun, J. (2015) · 2015 IEEE International Conference on Software Quality, Reliability and Security · doi:10.1109/qrs.2015.14 · Cited by 352
An empirical study of the impact of modern code review practices on software quality
McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2016) · Empirical Software Engineering · doi:10.1007/s10664-015-9381-9 · Cited by 332
Self-citationProgress on approaches to software defect prediction
Li, Z., Jing, X.-Y., & Zhu, X. (2018) · IET Software · doi:10.1049/iet-sen.2017.0148 · Cited by 264
Predicting Defective Lines Using a Model-Agnostic Technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137
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Metadata
- Authors
- Kamei, Y., Matsumoto, S., Monden, A., Matsumoto, K.-I., Adams, B., & Hassan, A. E.
- Venue
- 2010 IEEE International Conference on Software Maintenance
- Publisher
- IEEE
- Published
- 2010-09
- Pages
- 1-10
- ISSN
- 1063-6773 (ISSN-L)
- OpenAlex
- W2051978688
- Cited by
- 207
- References
- 1
- FWCI
- 7.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kamei, Y., Matsumoto, S., Monden, A., Matsumoto, K.-I., Adams, B., & Hassan, A. E. (2010). Revisiting common bug prediction findings using effort-aware models. 2010 IEEE International Conference on Software Maintenance. https://doi.org/10.1109/icsm.2010.5609530
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