Abstract
Code comments generation is a multi-disciplinary research, involving many different fields such as software engineering and natural language processing. In general, the automatic generation of code comments takes the source code as the input and its corresponding natural language description as outputs. In previous work, the code is regarded as a sequence and modeled by sequence models such as RNN, ignoring the data flow between variables and long-range dependencies in the code. Therefore, we propose a novel DFG-trans model to automatically generate code comments. Our model adds data flow guidance to the Transformer, which allows the model to focus on the flow of data between variables. This leads to better learning of semantic information in the code and alleviates the problem of long-distance dependencies. Experiments on a Java dataset show that our proposed model effectively improves the quality of code comments. Under various evaluation metrics, the scores of DFG-trans are better than some existing advanced models.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Jiangsu Province (21KJB520027).
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Zhou, W., Wu, J. (2022). Code Comments Generation with Data Flow-Guided Transformer. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_15
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