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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 15:42 - 15:54 at Silla - A3-Code Summarization Chair(s): Shaohua Wang

Source code summarization aims to automatically generate concise summaries of source code in natural language texts, in order to help developers better understand and maintain source code. Traditional work generates a source code summary by utilizing information retrieval techniques, which select terms from original source code or adapt summaries of similar code snippets. Recent studies adopt Neural Machine Translation techniques and generate summaries from code snippets using encoder-decoder neural networks. The neural-based approaches prefer the high-frequency words in the corpus and have trouble with the low-frequency ones. In this paper, we propose a retrieval-based neural source code summarization approach where we enhance the neural model with the most similar code snippets retrieved from the training set. Our approach can take advantages of both neural and retrieval-based techniques. Specifically, we first train an attentional encoder-decoder model based on the code snippets and the summaries in the training set; Second, given one input code snippet for testing, we retrieve its two most similar code snippets in the training set from the aspects of syntax and semantics, respectively; Third, we encode the input and two retrieved code snippets, and predict the summary by fusing them during decoding. We conduct extensive experiments to evaluate our approach and the experimental results show that our proposed approach can improve the state-of-the-art methods.

Tue 7 Jul

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15:00 - 16:00
A3-Code SummarizationTechnical Papers / New Ideas and Emerging Results at Silla
Chair(s): Shaohua Wang New Jersey Institute of Technology, USA
15:00
12m
Talk
Posit: Simultaneously Tagging Natural and Programming LanguagesTechnicalArtifact Available
Technical Papers
Profir-Petru Pârțachi University College London, Santanu Dash University College London, UK, Christoph Treude The University of Adelaide, Earl T. Barr University College London, UK
Pre-print Media Attached File Attached
15:12
12m
Talk
CPC: Automatically Classifying and Propagating Natural Language Comments via Program AnalysisTechnicalArtifact Available
Technical Papers
Juan Zhai Rutgers University, Xiangzhe Xu Nanjing University, Yu Shi Purdue University, Guanhong Tao Purdue University, Minxue Pan Nanjing University, Shiqing Ma Rutgers University, Lei Xu National Key Laboratory for Novel Software Technology, Nanjing University, Weifeng Zhang Nanjing University of Posts and Telecommunications, Lin Tan Purdue University, Xiangyu Zhang Purdue University
15:24
12m
Talk
Suggesting Natural Method Names to Check Name ConsistenciesTechnical
Technical Papers
Son Nguyen The University of Texas at Dallas, Hung Phan , Trinh Le University of Engineering and Technology, Tien N. Nguyen University of Texas at Dallas
Pre-print
15:36
6m
Talk
Where should I comment my code? A dataset and model for predicting locations that need commentsNIER
New Ideas and Emerging Results
Annie Louis University of Edinburgh, Santanu Dash University College London, UK, Earl T. Barr University College London, UK, Michael D. Ernst University of Washington, USA, Charles Sutton Google Research
15:42
12m
Talk
Retrieval-based Neural Source Code SummarizationTechnical
Technical Papers
Jian Zhang Beihang University, Xu Wang Beihang University, Hongyu Zhang University of Newcastle, Australia, Hailong Sun Beihang University, Xudong Liu Beihang University
Pre-print
15:54
6m
Talk
The Dual Channel HypothesisNIER
New Ideas and Emerging Results
Casey Casalnuovo University of California at Davis, USA, Earl T. Barr University College London, UK, Santanu Dash University College London, UK, Prem Devanbu University of California, Emily Morgan University of California, Davis