Tool Support for Code Change Inspection with Deep Learning in Evolving Software
Krishna Teja Ayinala, Kwok Sun Cheng, Kwangsung Oh, Myoungkyu Song · 2020
Similar changes are common during the development of a project. Many of the changes are similar but appears different based on the local context. During code review the code changes are inspected per each source file. The process of identifying similar code change is time-taking and error-prone. To overcome this problem, we propose Similar Changes Inspection with Deep Learning (SIL) which (1) creates a generalized edit script based on the data and control dependence to (2) identify and summarize similar code changes by (3) modelling a deep learning classifier. In order to train a classifier, we have identified clones of four types from a clone database mined from 25,000 programs. SIL summarizes the changes and identifies the change anomalies. To obtain feedback on the SIL approach, we have conducted an user study with seven Computer Science students. The study revealed that SIL helped these developers to conduct peer code reviews more effectively. SIL is available as an Eclipse plug-in and its demonstration video is available at https://sites.google.com/unomaha.edu/codereview-deeplearning.