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/\*Icomment: Bugs or Bad Comments?\*/ by Lin Tan; Ding Yuan; Gopal Krishna; Yuanyuan Zhou is a Computer Science article available to read on EtoBox.

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Commenting source code has long been a common practice in software development. Compared to source code, comments are more __direct__ , __descriptive__ and __easy-to-understand__ . Comments and sourcecode provide relatively redundant and independent information regarding a program's semantic behavior. As software evolves, they can easily grow out-of-sync, indicating two problems: (1) bugs -the source code does not follow the assumptions and requirements specified by correct program comments; (2) bad comments - comments that are inconsistent with correct code, which can confuse and mislead programmers to introduce bugs in subsequent versions. Unfortunately, as most comments are written in natural language, no solution has been proposed to automatically analyze commentsand detect inconsistencies between comments and source code. This paper takes the first step in automatically analyzing commentswritten in natural language to extract implicit program rulesand use these rules to automatically detect inconsistencies between comments and source code, indicating either bugs or bad comments. Our solution, iComment, combines Natural Language Processing(NLP), Machine Learning, Statistics and

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Author

Lin Tan; Ding Yuan; Gopal Krishna; Yuanyuan Zhou

Publisher

ACM

Published

2007

Language

EN

Field

Computer Science (Physical Sciences)