Abstract
Code comments are one of the important documents to help developers review and comprehend source code. In recent studies, researchers have proposed many deep learning models to generate the method header comments (i.e., method comment), which have achieved encouraging results. The comments in the method, which is called inline comment, are also important for program comprehension. Unfortunately, they have not received enough attention in automatic generation when comparing with the method comments. In this paper, we compare and analyze the similarities and differences between the method comments and the inline comments. By applying the existing models of generating method comments to the inline comment generation, we find that these existing models perform worse on the task of inline comment generation. We then further explore the possible reasons and obtain a number of new observations. For example, we find that there are a lot of templates (i.e., comments with the same or similar structures) in the method comment dataset, which makes the models perform better. Some terms were thought to be important (e.g., API calls) in the comment generation by previous study does not significantly affect the quality of the generated comments, which seems counter-intuitive. Our findings may give some implications for building the approaches of method comment or inline comment generation in the future.
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