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Learning Code Context Information to Predict Comment Locations
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DOI：10.1109/TR.2019.2931725.png)
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Abstract
En 中文
Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. In our developer survey, commenting has become an important, yet often-neglected activity when programming. Moreover, there is a lack of formal and automatic way in current practice to remind developers where to comment in the source code. To provide informative guidance on commenting during development, we propose a novel method CommentSuggester to recommend developers regarding appropriate commenting locations in the source code. Because commenting is closely related to the context information of source code, we identify this important factor to determine comment positions and extract it as structural context features, syntactic context features, and semantic context features. Subsequently, machine learning techniques are applied to identify possible commenting locations in the source code. We evaluated CommentSuggester using large datasets from dozens of open-source software systems in GitHub. The encouraging experimental results and user study demonstrated the feasibility and effectiveness of our commenting suggestion method.
Keywords:
Feature extraction
Software
Semantics
Programming
Predictive models
Syntactics
Buildings
Code context information
code features extraction
comment location
comment quality
commenting decision
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