Implementation of Git&#039;s Commit Message Complex Classification Model for Software Maintenance (소프트웨어 유지 관리를 위한 Git 커밋 메시지 복합 분류 모델 구현)

Ji-Hoon Choi (최지훈); Joon-Yong Kim (김준용); Seong-Hyun Park (박성현)

doi:10.9708/jksci.2022.27.11.131

Implementation of Git's Commit Message Complex Classification Model for Software Maintenance

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(11), pp.131-138
DOI : 10.9708/jksci.2022.27.11.131
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : August 31, 2022
Accepted : November 7, 2022
Published : November 30, 2022

Ji-Hoon Choi ¹, Joon-Yong Kim ², Seong-Hyun Park ³

¹공주대학교 컴퓨터공학과
²서울신학대학교
³공주대학교 컴퓨터공학과 박사과정

Accredited

ABSTRACT

Git's commit message is closely related to the project life cycle, and by this characteristic, it can greatly contribute to cost reduction and improvement of work efficiency by identifying risk factors and project status of project operation activities. Among these related fields, there are many studies that classify commit messages as types of software maintenance, and the maximum accuracy among the studies is 87%. In this paper, the purpose of using a solution using the commit classification model is to design and implement a complex classification model that combines several models to increase the accuracy of the previously published models and increase the reliability of the model. In this paper, a dataset was constructed by extracting automated labeling and source changes and trained using the DistillBERT model. As a result of verification, reliability was secured by obtaining an F1 score of 95%, which is 8% higher than the maximum of 87% reported in previous studies. Using the results of this study, it is expected that the reliability of the model will be increased and it will be possible to apply it to solutions such as software and project management.

KEYWORDS

Commit Message, Multi-Label Classification, DistilBERT(Bidirectional Encoder Representations from Transformers), Source Change, Auto Labeling

Citation status

* References for papers published after 2023 are currently being built.

[confproc] Mockus Votta / 2000 / Identifying reasons for software changes using historic databases / Proceedings 2000 International Conference on Software Maintenance : 120~130

[confproc] S. Gharbi / 2019 / On the Classification of Software Change Messages Using Multi-Label Active Learning / Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing 2019 : 1760~1767

[confproc] S. Levin / 2017 / 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 : 97~106

[other] A. Adhikari / 2019 / DocBERT: BERT for Document Classification

[confproc] M. U. Sarwar / 2020 / Multi-label Classification of Commit Messages using Transfer Learning / 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) : 37~42

[journal] H. C. Gall / 2009 / hange analysis with evolizer and changedistiller / IEEE Software 26(1) : 26~

[confproc] B. Fluri / 2008 / Discovering patterns of change types / Automated Software Engineering, 2008. ASE 2008. 23rd IEEE/ACM International Conference on / IEEE : 463~466

[other] M. Martinez / 2013 / Automatically extracting instances of code change patterns with ast analysis

[journal] B. Fluri / 2007 / Change distilling: Tree differencing for fine-grained source code change extraction / Software Engineering, IEEE Transactions 33(11) : 725~743

[other] V. Sanh / 2019 / Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter

[confproc] Devlin, Jacob / 2019 / 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 1 : 4171~4186

[journal] Sun, Chi / 2019 / How to Fine-Tune BERT for Text Classification? / Lecture Notes in Computer Science : 194~206

[confproc] A. Mauczka / 2015 / Dataset of developer-labeled commit messages / 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories / IEEE : 490~493

[confproc] S. Zafar / 2019 / Towards standardizing and improving classification of bug-fix commits / 2019ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) / IEEE : 1~6

[other] Hultstrand, S / 2015 / Git-CLI or GUI: Which is most widely used and why?

[other] Miller, C. G / 2022 / Introduction to Git

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Git's Commit Message Complex Classification Model for Software Maintenance

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Git's Commit Message Complex Classification Model for Software Maintenance

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

REFERENCES (16) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.