Write a Blog >>
ICSE 2022
Sun 8 - Fri 27 May 2022
Wed 11 May 2022 04:15 - 04:20 at ICSE room 3-even hours - Recommender Systems 1 Chair(s): Alessio Ferrari
Wed 11 May 2022 13:15 - 13:20 at ICSE room 5-odd hours - Recommender Systems 2 Chair(s): Gabriele Bavota

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender’s performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one \emph{Pull-Request (PR)} (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one~\emph{PR} with multiple reviewers herein) and developed a new recommender, namely~\emph{HGRec}, which is evaluated by 12 OSS projects with more than 87K \emph{PR}s, 680K comments in terms of~\emph{accuracy} and~\emph{recommendation distribution}. The results indicate that~\emph{HGRec} outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders,~\emph{HGRec} is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers’ workload congestion issue. Moreover, since~\emph{HGRec} is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.

Wed 11 May

Displayed time zone: Eastern Time (US & Canada) change

04:00 - 05:00
04:00
5m
Talk
Predicting the Objective and Priority of Issue Reports in Software Repositories
Journal-First Papers
Maliheh Izadi Sharif University of Technology, Kiana Akbari Sharif University of technology, Abbas Heydarnoori Sharif University of Technology
Link to publication DOI Pre-print Media Attached
04:05
5m
Talk
Code Reviewer Recommendation in Tencent: Practice, Challenge, and Direction
SEIP - Software Engineering in Practice
Qiuyuan Chen Zhejiang University, Dezhen Kong Zhejiang University, Lingfeng Bao Zhejiang University, Chenxing Sun Tencent, Xin Xia Huawei Software Engineering Application Technology Lab, Shanping Li Zhejiang University
Pre-print Media Attached
04:10
5m
Talk
Using Deep Learning to Generate Complete Log Statements
Technical Track
Antonio Mastropaolo Università della Svizzera italiana, Luca Pascarella Università della Svizzera italiana (USI), Gabriele Bavota Software Institute, USI Università della Svizzera italiana
Pre-print Media Attached
04:15
5m
Talk
Modeling Review History for Reviewer Recommendation: A Hypergraph Approach
Technical Track
Guoping Rong Nanjing University, YiFan Zhang Nanjing University, Lanxin Yang Nanjing University, Fuli Zhang Nanjing University, Hongyu Kuang Nanjing University, He Zhang Nanjing University
Pre-print Media Attached
04:20
5m
Talk
ShellFusion: Answer Generation for Shell Programming Tasks via Knowledge Fusion
Technical Track
Neng Zhang School of Software Engineering, Sun Yat-sen University, Chao Liu Zhejiang University, Xin Xia Huawei Software Engineering Application Technology Lab, Christoph Treude University of Melbourne, Ying Zou Queen's University, Kingston, Ontario, David Lo Singapore Management University, Zibin Zheng School of Data and Computer Science, Sun Yat-sen University
DOI Pre-print Media Attached
04:25
5m
Talk
CLEAR: Contrastive Learning for API Recommendation
Technical Track
Moshi Wei York University, Nima Shiri harzevili York University, Yuchao Huang Institute of Software Chinese Academy of Sciences, Junjie Wang Institute of Software at Chinese Academy of Sciences, Song Wang York University
Pre-print Media Attached
13:00 - 14:00
Recommender Systems 2Technical Track / NIER - New Ideas and Emerging Results / SEIP - Software Engineering in Practice at ICSE room 5-odd hours
Chair(s): Gabriele Bavota Software Institute, USI Università della Svizzera italiana
13:00
5m
Talk
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
NIER - New Ideas and Emerging Results
Martin Weyssow DIRO, Université de Montréal, Houari Sahraoui Université de Montréal, Bang Liu DIRO & Mila, Université de Montréal
Pre-print Media Attached
13:05
5m
Talk
Code Reviewer Recommendation in Tencent: Practice, Challenge, and Direction
SEIP - Software Engineering in Practice
Qiuyuan Chen Zhejiang University, Dezhen Kong Zhejiang University, Lingfeng Bao Zhejiang University, Chenxing Sun Tencent, Xin Xia Huawei Software Engineering Application Technology Lab, Shanping Li Zhejiang University
Pre-print Media Attached
13:10
5m
Talk
Recommending Good First Issues in GitHub OSS Projects
Technical Track
Wenxin Xiao School of Computer Science, Peking University, Hao He Peking University, Weiwei Xu School of Computer Science and Technology, Soochow University, Xin Tan Beihang University, China, Jinhao Dong Peking University, Minghui Zhou Peking University, China
Pre-print Media Attached
13:15
5m
Talk
Modeling Review History for Reviewer Recommendation: A Hypergraph Approach
Technical Track
Guoping Rong Nanjing University, YiFan Zhang Nanjing University, Lanxin Yang Nanjing University, Fuli Zhang Nanjing University, Hongyu Kuang Nanjing University, He Zhang Nanjing University
Pre-print Media Attached
13:20
5m
Talk
ShellFusion: Answer Generation for Shell Programming Tasks via Knowledge Fusion
Technical Track
Neng Zhang School of Software Engineering, Sun Yat-sen University, Chao Liu Zhejiang University, Xin Xia Huawei Software Engineering Application Technology Lab, Christoph Treude University of Melbourne, Ying Zou Queen's University, Kingston, Ontario, David Lo Singapore Management University, Zibin Zheng School of Data and Computer Science, Sun Yat-sen University
DOI Pre-print Media Attached
13:25
5m
Talk
Using Deep Learning to Generate Complete Log Statements
Technical Track
Antonio Mastropaolo Università della Svizzera italiana, Luca Pascarella Università della Svizzera italiana (USI), Gabriele Bavota Software Institute, USI Università della Svizzera italiana
Pre-print Media Attached

Information for Participants
Wed 11 May 2022 04:00 - 05:00 at ICSE room 3-even hours - Recommender Systems 1 Chair(s): Alessio Ferrari
Info for room ICSE room 3-even hours:

Click here to go to the room on Midspace

Wed 11 May 2022 13:00 - 14:00 at ICSE room 5-odd hours - Recommender Systems 2 Chair(s): Gabriele Bavota
Info for room ICSE room 5-odd hours:

Click here to go to the room on Midspace