1 |
B. Fluri, E. Giger, and H. C. Gall, "Discovering patterns of change types," in Automated Software Engineering, 2008. ASE 2008. 23rd IEEE/ACM International Conference on. IEEE, 2008, pp. 463-466. DOI: 10.1109/ASE.2008.74
DOI
|
2 |
M. Martinez, L. Duchien, and M. Monperrus, "Automatically extracting instances of code change patterns with ast analysis," arXiv preprint arXiv:1309.3730, 2013. DOI: 10.1109/ICSM.2013.54
DOI
|
3 |
V. Sanh, L. Debut, J. Chaumond, and T. Wolf, "Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter," arXiv preprint arXiv:1910.01108, 2019. DOI: 10.48550/arXiv.1910.01108
|
4 |
A. Mauczka, F. Brosch, C. Schanes, and T. Grechenig, "Dataset of developer-labeled commit messages," in 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. IEEE, 2015, pp. 490-493. DOI: 10.1109/MSR.2015.71
DOI
|
5 |
S. Zafar, M. Z. Malik, and G. S. Walia, "Towards standardizing and improving classification of bug-fix commits," in 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 2019, pp. 1-6. DOI: 10.1109/ESEM.2019.8870174
DOI
|
6 |
Miller, C. G. (2022). Introduction to Git.
|
7 |
M. U. Sarwar, S. Zafar, M. W. Mkaouer, G. S. Walia and M. Z. Malik, "Multi-label Classification of Commit Messages using Transfer Learning," 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pp. 37-42, 2020, doi: 10.1109/ISSREW51248.2020.00034.
DOI
|
8 |
Mockus and Votta, "Identifying reasons for software changes using historic databases," Proceedings 2000 International Conference on Software Maintenance, pp. 120-130, 2000. doi:10.1109/ICSM.2000.883028.
DOI
|
9 |
S. Levin, and A. Yehudai, "Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changes," PROMISE: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 97-106, November. 2017. doi: 10.1145/3127005.3127016
DOI
|
10 |
A. Adhikari, A. Ram, R. Tang, and J. Lin, "DocBERT: BERT for Document Classification," arXiv, 2019. doi: 10.48550/ARXIV.1904.08398
|
11 |
H. C. Gall, B. Fluri, and M. Pinzger, "hange analysis with evolizer and changedistiller," IEEE Software, vol. 26, no. 1, p. 26, 2009. DOI: 10.1109/MS.2009.6
DOI
|
12 |
B. Fluri, M. Wursch, M. PInzger, and H. C. Gall, "Change distilling: Tree differencing for fine-grained source code change extraction," Software Engineering, IEEE Transactions on, vol. 33, no. 11, pp. 725-743, 2007. DOI: 10.1109/TSE.2007.70731
DOI
|
13 |
Sun, Chi, et al. "How to Fine-Tune BERT for Text Classification?" Lecture Notes in Computer Science, 2019, pp. 194-206. DOI:10.1007/978-3-030-32381-3_16
DOI
|
14 |
Hultstrand, S., & Olofsson, R. (2015). Git-CLI or GUI: Which is most widely used and why?.
|
15 |
Devlin, Jacob, et al. "BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, June 2019, pp. 4171-4186. DOI:10.18653/v1/N19-1423
DOI
|
16 |
S. Gharbi, M. W. Mkaouer, I. Jenhani, and M. B. Messaoud, "On the Classification of Software Change Messages Using Multi-Label Active Learning," in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019, pp. 1760-1767. 2019. doi: 10.1145/3297280.3297452
DOI
|