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CodeAttention: translating source code to comments by exploiting the code constructs

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Abstract

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code. Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if coding patterns change and hence the performance of comment generation declines. In addition, recent approaches ignore exploiting the code constructs and leveraging the code snippets like plain text. Furthermore, previous datasets are also too small to validate the methods and show their advantage. In this paper, we propose a new attention mechanism called CodeAttention to translate code to comments, which is able to utilize the code constructs, such as critical statements, symbols and keywords. By focusing on these specific points, CodeAttention could understand the semantic meanings of code better than previous methods. To verify our approach in wider coding patterns, we build a large dataset from open projects in GitHub. Experimental results in this large dataset demonstrate that the proposed method has better performance over existing approaches in both objective and subjective evaluation. We also perform ablation studies to determine effects of different parts in CodeAttention.

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Acknowledgements

This research was supported by the National Key Research and Development Program (2017YFB1001903) and the National Natural Science Foundation of China (Grant No. 61422304).

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Wenhao Zheng, Hongyu Zhou, Ming Li & Jianxin Wu

Authors
  1. Wenhao Zheng
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  2. Hongyu Zhou
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  3. Ming Li
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  4. Jianxin Wu
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Corresponding author

Correspondence to Ming Li.

Additional information

Wenhao Zheng received the BS degree in Computer Science and Technology from Nanjing University, China in 2015. In the same year, he was admitted to study for a MS degree in Nanjing University without entrance examination. He is currently a member of the LAMDA group. His research interests mainly include machine learning and software mining. Mr. Zheng received National Scholarship in 2014.

Hongyu Zhou received his BE degree from Wuhan University, China in 2015. He is currently a postgraduate student in the Department of Computer Science and Technology, Nanjing University, China. His research interests include computer vision and machine learning.

Ming Li received the BS and PhD degrees in computer science from Nanjing University, China in 2003 and 2008 respectively. He is currently an assistant professor with LAMDA Group, the Department of Computer Sciences and Technology, Nanjing University. His major research interests include machine learning, data mining and information retrieval, especially on learning with labeled and unlabeled data. He has been granted various awards including the CCF Outstanding Doctoral Dissertation Award (2009), Microsoft Fellowship Award (2005), etc. He has served on the program committee of a number of important international conferences including KDD’10, ACML’10, ACML’09, ACM CKIM’09, IEEE ICME’10, AI’10, etc. and served as reviewers for a number of journals including IEEE Trans. KDE, IEEE Trans. NN, IEEE Trans. SMCC, ACM Trans. IST, Pattern Recognition, Knowledge and Information Systems, Journal of Computer Science and Technology, etc. He is a committee member of CAAI machine learning society, member of ACM, IEEE, IEEE computer society, CCF and CAAI.

Jianxin Wu received his BS and MS degrees in computer science from Nanjing University, and his PhD degree in computer science from the Georgia Institute of Technology. He is currently a professor in the Department of Computer Science and Technology at Nanjing University, China, and is associated with the National Key Laboratory for Novel Software Technology, China.

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Zheng, W., Zhou, H., Li, M. et al. CodeAttention: translating source code to comments by exploiting the code constructs. Front. Comput. Sci. 13, 565–578 (2019). https://doi.org/10.1007/s11704-018-7457-6

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