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Mining Question and Answer Sites for Automatic Comment Generation

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Wong_Edmund.pdf (366.7 KB)

Date

2014-04-29

Authors

Edmund, Wong

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University of Waterloo

Abstract

Code comments improve software maintainability, programming productivity, and software reliability. To address the comment scarcity issue in many projects and save developers’ time in writing comments, we propose a new, general automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings. We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open source projects. We apply AutoComment to analyze 92,140 Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment generate 102 comments automatically for 23 Java and Android projects. The number of generated comments is still low, but the user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code. One of the advantages from mining Q&A sites for automatic comment generation is that human written comments can provide information that is not explicitly in the code. In the future, we would like to focus on improving both the yield and quality of the generated comments. To improve the yield, we can replace the token-based clone detection tool with one that can detect addition and reordering of lines to increase the number of code matches. To improve the quality, we can apply advanced natural language processing techniques such as semantic role labeling to analyze the semantics of the sentences, or typed dependencies to analyze the grammatical structure of the sentences.

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Keywords

documentation, comments, natural language processing, comment generation

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URI

http://hdl.handle.net/10012/8381

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Theses
Electrical and Computer Engineering