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Studying just-in-time defect prediction using cross-project models
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Cites
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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
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Cited by
Deep Semantic Feature Learning for Software Defect Prediction
Wang, S., Liu, T., Nam, J., & Tan, L. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2877612 · Cited by 320
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
Hosseini, S., Turhan, B., & Gunarathna, D. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2770124 · Cited by 317
Progress 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
Exploring the Capabilities of LLMs for Code-Change-Related Tasks
Fan, L., Liu, J., Liu, Z., Lo, D., Xia, X., & Li, S. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709358 · Cited by 41
“To deal with code-change-related tasks, prior studies have proposed a series of machine-learning-based [29, 31, 65] and deep-learning-based approaches [23, 40, 51].”
(2023) · doi:10.1109/ase56229.2023.00157 · Cited by 14
Metadata
- Authors
- Kamei, Y., Fukushima, T., McIntosh, S., Yamashita, K., Ubayashi, N., & Hassan, A. E.
- Venue
- Empirical Software Engineering
- Published
- 2015-09-14
- Volume
- 21
- Issue
- 5
- Pages
- 2072-2106
- ISSN
- 1382-3256 (ISSN-L)
- 1573-7616
- OpenAlex
- W2276400542
- Cited by
- 236
- References
- 2
- FWCI
- 33.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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