• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.123-125
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• 2022

Git의 커밋 메시지는 프로젝트가 진행되면서 발생하는 각종 이슈 및 코드의 변경이력을 저장하고 관리하고 있기 때문에 소프트웨어 유지관리와 프로젝트의 생명주기와 밀접한 연관성을 갖고 있다. 이러한 Git의 커밋 메시지에 대한 정확한 분석 결과는 소프트웨어 개발 및 유지관리 활동 시, 시간과 비용의 효율적인 관리에 많은 영향을 끼치고 있다. 이에 대한 기존 연구로 Git에서 발생하는 커밋 메시지를 소프트웨어 유지관리의 세 가지 형태로 분류하고 매핑하여 정확한 분석을 시도하려는 연구가 진행되었으나, 최대 87%의 정확도를 제시한 연구 결과가 있었다. 이러한 연구들은 정확도가 낮아 실제 프로젝트의 개발 및 유지관리에 적용하기에는 위험성과 어려움이 있는 현실이다. 본 논문에서는 커밋 메시지 분류에 대한 선행 연구 조사를 통해 각 연구들의 프로세스와 특징을 추출하였고, 이를 이용한 분류 정확도를 높일 수 있는 커밋 복합 분류 모델에 대해 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.81-83
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• 2023

GIT의 커밋 메시지를 소프트웨어 유지보수 활동 세 가지로 분류하는 연구를 분석하고 정확도를 높일 수 있는 모델들을 분석하였고 관련 모델 중 커밋메시지와 변경된 소스를 같이 활용하는 연구들은 변경된 소스를 분석하기 위해 도구들을 대부분 활용하는데 대부분 특정 언어만 분류할 수 있는 한계가 있다. 본 논문에서는 소스 변경 데이터를 추출할 때 언어의 제약을 없애기 위해 GPT를 이용해 변경된 소스의 요약을 추출하는 과정을 추가함으로써 언어 제약의 한계를 극복할 수 있는 개선된 모델에 관한 연구를 진행하였다. 향후 본 연구 모델의 구현 및 검증을 진행하고 이를 이용해 프로젝트 진행에 활용할 수 있는 솔루션 개발 연구까지 확정해 나갈 예정이다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.131-138
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• 2022

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.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.235-239
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• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.754-764
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• 2022

Code refactoring refers to a maintenance task to change the code of a software system in order to consider new requirements, fix bugs, and restructure code. There have been various studies of refactoring subjects such as refactoring types, refactoring benefits, and CASE tools. However, Java applications rather than python ones have been benefited by refactoring-based coding practices. There are few cases of refactoring stuides on Python applications. This paper finds and analyzes single refactoring operations and composite refactoring operations for Python-based deep learning systems. In addition, we find that there is a statistically significant difference in the frequency of occurrence of single and complex refactoring operations in the two groups of deep learning applications and typical Python applications. Furthermore, we analyze keywords of commit messages to catch refactoring intentions of software developers.