Poster: Guiding Developers to Make Informative Commenting Decisions in Source Code

Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. However, there is a lack of thorough specifications to help developers make their commenting decisions in current practice. To reduce the effort of making commenting decisions, we propose a novel method, CommentSuggester, to guide developers regarding appropriate commenting locations in the source code. We extract context information of source code and employ machine learning techniques to identify possible commenting locations in the source code. The encouraging experimental results demonstrated the feasibility and effectiveness of our commenting suggestion method.