Code Edit Recommendation Using a Recurrent Neural Network

Applied Sciences

Jaejun Lee¹  Seonah Lee²  Heetae Cho²  Jongsun Ahn³  Sungwon Kang³ 

[1] BigPictureLabs Inc., Daejeon 34047, Korea;Department of AI Convergence Engineering (Graduate) and Aerospace and Software Engineering (Undergraduate), Gyeongsang National University, Jinju 52828, Korea;School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea;

关键词: data-based software engineering;    code edit recommendation;    recurrent neural network;    machine learning;    interaction histories;   

DOI  :  10.3390/app11199286

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown