Automatically Detecting the Scopes of Source Code Comments

Comments which are an integral part of software development improve program comprehension and software maintainability. They convey useful information about the system functionalities and many text retrieval methods for software engineering tasks take comments as an important source for code semantic analysis. However, it is challenging to identify the relationship between the functional semantics of the code and its corresponding textual descriptions and apply it to automatic mining approaches in software engineering efficiently. In this paper, we use machine learning which utilizes features of code snippets and comments to detect the scopes of source code comments automatically in Java programs. Based on the dataset of comment-statement pairs from 4 popular open source projects, our method achieved a high accuracy of 81.15% in detecting the scopes of comments. Furthermore, the experimental results demonstrated the feasibility and effectiveness of our comment scope detection method.