Learning Code Changes by Exploiting Bidirectional Converting Deviation

摘要

Software systems evolve with constant code changes when requirements change or bugs are found. Assessing the quality of code change is a vital part of software development. However, most existing software mining methods inspect software data from a static view and learn global code semantics from a snapshot of code, which cannot capture the semantic information of small changes and are under{-}representation for rich historical code changes. How to build a model to emphasize the code change remains a great challenge. In this paper, we propose a novel deep neural network called CCL, which models a forward converting process from the code before change to the code after change and a backward converting process inversely, and the change representations of bidirectional converting processes can be learned. By exploiting the deviation of the converting processes, the code change can be evaluated by the network. Experimental results on open source projects indicate that CCL significantly outperforms the compared methods in code change learning.