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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Thu 18 May 2023 12:00 - 12:15 at Meeting Room 103 - Code review Chair(s): Thomas LaToza

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. Fortunately, at Microsoft, we have a wealth of work artifacts across many repositories that can yield valuable information about our developers. To address the aforementioned problem, 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 (PRs), 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 within Microsoft. 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.”

Thu 18 May

Displayed time zone: Hobart change

11:00 - 12:30
11:00
15m
Talk
Workflow analysis of data science code in public GitHub repositories
Journal-First Papers
Dhivyabharathi Ramasamy Department of Informatics, University of Zurich, Zurich, Switzerland, Cristina Sarasua Department of Informatics, University of Zurich, Zurich, Switzerland, Alberto Bacchelli University of Zurich, Abraham Bernstein Department of Informatics, University of Zurich, Zurich, Switzerland
11:15
15m
Talk
Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension
Journal-First Papers
Haytham Hijazi CISUC, DEI, University of Coimbra, João Durães CISUC, Polytechnic Institute of Coimbra, Ricardo Couceiro University of Coimbra, Raul Barbosa CISUC, DEI, University of Coimbra, João Castelhano ICNAS, University of Coimbra, Júlio Medeiros CISUC, DEI, University of Coimbra, Miguel Castelo Branco ICNAS/CIBIT, University of Coimbra, Paulo Carvalho University of Coimbra, Henrique Madeira University of Coimbra
11:30
15m
Talk
Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions
Technical Track
Mahtab Nejati University of Waterloo, Mahmoud Alfadel University of Waterloo, Shane McIntosh University of Waterloo
Pre-print
11:45
15m
Talk
Please fix this mutant: How do developers resolve mutants surfaced during code review?
SEIP - Software Engineering in Practice
Goran Petrovic Google; Universität Passau, René Just University of Washington, Marko Ivanković Google; Universität Passau, Gordon Fraser University of Passau
12:00
15m
Talk
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft
SEIP - Software Engineering in Practice
Jiyang Zhang University of Texas at Austin, Chandra Maddila Microsoft Research, Ram Bairi Microsoft Research, Christian Bird Microsoft Research, Ujjwal Raizada Microsoft Research, Apoorva Agrawal Microsoft Research, Yamini Jhawar Microsoft Research, Kim Herzig Microsoft, Arie van Deursen Delft University of Technology
Pre-print Media Attached
12:15
7m
Talk
A mixed-methods analysis of micro-collaborative coding practices in OpenStack
Journal-First Papers
Armstrong Foundjem Queen's University, Eleni Constantinou University of Cyprus, Tom Mens University of Mons, Bram Adams Queen's University, Kingston, Ontario