Code Edit Recommendation Using a Recurrent Neural Networkopen access
- Lee, Seonah; Lee, Jaejun; Kang, Sungwon; Ahn, Jongsun; Cho, Heetae
- Issue Date
- data-based software engineering; code edit recommendation; recurrent neural network; machine learning; interaction histories
- APPLIED SCIENCES-BASEL, v.11, no.19
- Journal Title
- APPLIED SCIENCES-BASEL
- When performing software evolution tasks, developers spend a significant amount of time looking for files to modify. By recommending files to modify, a code edit recommendation system reduces the developer's navigation time when conducting software evolution tasks. In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN). CERNN forms contexts that maintain the sequence of developers' interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution task. We evaluated our method by comparing it with the state-of-the-art method MI-EA that was developed based on the association rule mining technique. The result shows that our proposed method improves the average recommendation accuracy by approximately 5% over MI-EA (0.64 vs. 0.59 F-score).
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- 공과대학 > Department of Aerospace and Software Engineering > Journal Articles
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