Using Large-scale Heterogeneous Graph Representation Learning for Code
Review Recommendations
- URL: http://arxiv.org/abs/2202.02385v1
- Date: Fri, 4 Feb 2022 20:58:54 GMT
- Title: Using Large-scale Heterogeneous Graph Representation Learning for Code
Review Recommendations
- Authors: Jiyang Zhang, Chandra Maddila, Ram Bairi, Christian Bird, Ujjwal
Raizada, Apoorva Agrawal, Yamini Jhawar, Kim Herzig, Arie van Deursen
- Abstract summary: We present CORAL, a novel approach to reviewer recommendation.
We use a socio-technical graph built from the rich set of entities.
We show that CORAL is able to model the manual history of reviewer selection remarkably well.
- Score: 7.260832843615661
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Code review is an integral part of any mature software development process,
and identifying the best reviewer for a code change is a well accepted problem
within the software engineering community. Selecting a reviewer who lacks
expertise and understanding can slow development or result in more defects. To
date, most reviewer recommendation systems rely primarily on historical file
change and review information; those who changed or reviewed a file in the past
are the best positioned to review in the future. We posit that while these
approaches are able to identify and suggest qualified reviewers, they may be
blind to reviewers who have the needed expertise and have simply never
interacted with the changed files before. To address this, we present CORAL, a
novel approach to reviewer recommendation that leverages a socio-technical
graph built from the rich set of entities (developers, repositories, files,
pull requests, work-items, etc.) and their relationships in modern source code
management systems. We employ a graph convolutional neural network on this
graph and train it on two and a half years of history on 332 repositories. We
show that CORAL is able to model the manual history of reviewer selection
remarkably well. Further, based on an extensive user study, we demonstrate that
this approach identifies relevant and qualified reviewers who traditional
reviewer recommenders miss, and that these developers desire to be included in
the review process. Finally, we find that "classical" reviewer recommendation
systems perform better on smaller (in terms of developers) software projects
while CORAL excels on larger projects, suggesting that there is "no one model
to rule them all."
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