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ICSE 2022
Sun 8 - Fri 27 May 2022
Tue 10 May 2022 20:20 - 20:25 at ICSE room 5-even hours - Configurations and Recommendations Chair(s): Candy Pang
Wed 11 May 2022 04:25 - 04:30 at ICSE room 3-even hours - Recommender Systems 1 Chair(s): Alessio Ferrari

To help with API search, many automated API recommendation approaches have been proposed, and most of them leverage both Stack Overflow (SO) posts and API documentation to recommend APIs for a given natural language described programming task query. There are two orthogonal approaches for this task, i.e., information retrieval based and neural-based methods. Although these approaches have achieved remarkable performance, our observation shows that existing approaches can fail due to the following two reasons: 1) most information retrieval based approaches treat task queries as bags-of-words, which cannot capture the semantic-related sequential information, e.g., it is difficult for these approaches to distinguish the query \textit{convert int to string} from \textit{convert string to int}. 2) both the information retrieval based and the neural-based approaches are weak at distinguishing the semantic difference among lexically similar queries.

In this paper, we propose CLEAR, which leverages BERT sentence embedding and contrastive learning to tackle the above two issues of existing API recommendation approaches. Specifically, CLEAR embeds the whole sentence of posts with a BERT-based model rather than the bag-of-word model, which can preserve the semantic-related sequential information. In addition, CLEAR uses contrastive learning to learn the semantic representation of programming terminologies in different programming tasks. {Finally, CLEAR leverages the similarity between a given query and SO posts to recommend APIs for the query.} Our experiment results on three different test datasets confirm the effectiveness of CLEAR for both method-level and class-level API recommendation. Compared to the state-of-the-art API recommendation approaches, CLEAR improves the MAP by 25%-187% at method-level and 10%-100% at class-level.

Tue 10 May

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

20:00 - 21:00
20:00
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
20:05
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
20:10
5m
Talk
Dozer: Migrating Shell Commands to Ansible Modules via Execution Profiling and Synthesis
SEIP - Software Engineering in Practice
Eric Horton North Carolina State University, Chris Parnin North Carolina State University
Pre-print Media Attached
20:15
5m
Talk
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph
Technical Track
Wei Cheng Nanjing University, XiangRong Zhu Nanjing University, Wei Hu Nanjing University
DOI Pre-print Media Attached
20:20
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

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

Information for Participants
Tue 10 May 2022 20:00 - 21:00 at ICSE room 5-even hours - Configurations and Recommendations Chair(s): Candy Pang
Info for room ICSE room 5-even hours:

Click here to go to the room on Midspace

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