Software change‐proneness prediction based on deep learning. Issue 4 (17th February 2022)

• Record Type: Journal Article
• Authors: Zhu, Xiaoyan; Li, Nan; Wang, Yong
• Is Part Of: Journal of software. Volume 34:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-17
• Subjects: class imbalance -- CNN -- deep learning -- resampling -- software change‐proneness prediction; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2434
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Software change‐proneness prediction can help reduce software maintenance costs. Thus, it has drawn the attention of many researchers. In this paper, we propose a CNN (convolutional neural network)‐based method for software change‐proneness prediction, aiming to utilize the powerful prediction ability to make score of the performance measure higher than other baseline methods. Moreover, to alleviate the effect of the class imbalance problem, resampling methods are employed with the CNN. To validate the performance of the proposed CNN‐based method, an empirical study was conducted. The experimental results show that the CNN‐based method together with the resampling method performs better than the baseline methods, and the scores of performance measure of CNN with the ROS (random oversampling) method are higher than other method, especially the important performance measure. Abstract : In this paper, we propose a convolutional neural network (CNN)‐based method for software change‐proneness prediction, aiming to utilize the powerful prediction ability of CNN to achieve better performance. Moreover, to alleviate the effect of the class imbalance problem, resampling methods are employed with the CNN. To validate the performance of the proposed CNN‐based method, an empirical study was conducted. The experimental results show that the CNN‐based method together with the resampling method performs better than baseline methods.