Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2019

Abstract

Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall correlation between two pieces of source code, but not for the “difference” between two pieces of source code. In this paper, we propose a novel deep model named DACE for automatic code review. Such a model is able to learn revision features by contrasting the revised hunks from the original and revised source code with respect to the code context containing the hunks. Experimental results on six open source software projects indicate by learning the revision features, DACE can outperform the competing approaches in automatic code review.

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, January 27 - February 1

Volume

33

First Page

4910

Last Page

4917

Identifier

10.1609/aaai.v33i01.33014910

City or Country

Honolulu, Hawaii

Additional URL

https://doi.org/10.1609/aaai.v33i01.33014910

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