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ICPC 2022
Mon 16 - Tue 17 May 2022
co-located with ICSE 2022
Sun 15 May 2022 21:44 - 21:51 at ICPC room - Session 1: Summarization Chair(s): Haipeng Cai

Source code summarization aims to generate natural language descriptions of code snippets that can help developers comprehend the code and save time reading it. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code. Traditional models traverse ASTs as sequences or split ASTs into paths as input. However, the former loses the structural properties of ASTs, and the latter destroys the overall structure of ASTs. Therefore, comprehensively capturing the structural features of ASTs in learning code representations for source code summarization remains a challenging problem to be solved. In this paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer for source code Summarization. M2TS uses a multi-scale AST feature extraction method, where multi-scale refers to the different power matrices obtained from the AST adjacency matrix. This method represents ASTs at multiple local and global levels, which can extract the structure of ASTs more completely and accurately. To complement missing semantic information in ASTs, we also obtain source code features, and further combine them with the extracted AST features using a cross modality fusion method that not only fuses the syntactic and contextual semantic information of source code, but also highlights the key features of each modality. In particular, cross modality fusion is a follow-up work to the multi-scale method, so the latter contributes to the former. We conduct experiments on two Java and one Python datasets, and the experimental results demonstrate that M2TS outperforms current state-of-the-art methods. We make our code publicly available at https://github.com/TranSMS/M2TS.

Sun 15 May

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

21:30 - 22:20
Session 1: SummarizationResearch at ICPC room
Chair(s): Haipeng Cai Washington State University, USA
21:30
7m
Talk
PTM4Tag: Sharpening Tag Recommendation of Stack Overflow with Pre-trained Models
Research
Junda He Singapore Management University, Bowen Xu Singapore Management University, Zhou Yang Singapore Management University, DongGyun Han Singapore Management University, Chengran Yang Singapore Management University, David Lo Singapore Management University
Media Attached
21:37
7m
Talk
GypSum: Learning Hybrid Representations for Code Summarization
Research
Yu Wang School of Data Science and Engineering, East China Normal University, Yu Dong School of Data Science and Engineering, East China Normal University, Xuesong Lu School of Data Science and Engineering, East China Normal University, Aoying Zhou East China Normal University
DOI Pre-print Media Attached
21:44
7m
Talk
M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization
Research
Yuexiu Gao Shandong Normal University, Chen Lyu Shandong Normal University
Media Attached
21:51
7m
Talk
Semantic Similarity Metrics for Evaluating Source Code Summarization
Research
Sakib Haque University of Notre Dame, Zachary Eberhart University of Notre Dame, Aakash Bansal University of Notre Dame, Collin McMillan University of Notre Dame
Media Attached
21:58
7m
Talk
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition
Research
Rishab Sharma University of British Columbia, Fuxiang Chen University of British Columbia, Fatemeh Hendijani Fard University of British Columbia
Pre-print Media Attached
22:05
15m
Live Q&A
Q&A-Paper Session 1
Research


Information for Participants
Sun 15 May 2022 21:30 - 22:20 at ICPC room - Session 1: Summarization Chair(s): Haipeng Cai
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