The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction
Abstract
Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments on two popular software projects (i.e., QT and OPENSTACK) on three evaluation settings (i.e., cross-validation, short-period, and long-period) show that the best variant of DeepJIT (DeepJIT-Combined), compared with the best performing state-of-the-art approach, achieves improvements of 10.36-11.02% for the project QT and 9.51-13.69% for the project OPENSTACK in terms of the Area Under the Curve (AUC).
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Cites
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Cited by
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Watson, C., Cooper, N., Palacio, D. N., Moran, K., & Poshyvanyk, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3485275 · Cited by 137
CCT5: A Code-Change-Oriented Pre-trained Model
Lin, B., Wang, S., Liu, Z., Liu, Y., Xia, X., & Mao, X. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616339 · Cited by 69
(2023) · doi:10.1109/ase56229.2023.00157 · Cited by 14
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Metadata
- Authors
- Hoang, T., Khanh Dam, H., Kamei, Y., Lo, D., & Ubayashi, N.
- Venue
- 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 34-45
- OpenAlex
- W2955991060
- Cited by
- 223
- References
- 11
- FWCI
- 17.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hoang, T., Khanh Dam, H., Kamei, Y., Lo, D., & Ubayashi, N. (2019). DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction. 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). https://doi.org/10.1109/msr.2019.00016
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