The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Improved Code Summarization via a Graph Neural Network
Open accessAbstract
Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization techniques use the source code as input and outputs a natural language description. Yet a strong consensus is developing that using structural information as input leads to improved performance. The first approaches to use structural information flattened the AST into a sequence. Recently, more complex approaches based on random AST paths or graph neural networks have improved on the models using flattened ASTs. However, the literature still does not describe the using a graph neural network together with source code sequence as separate inputs to a model. Therefore, in this paper, we present an approach that uses a graph-based neural architecture that better matches the default structure of the AST to generate these summaries. We evaluate our technique using a data set of 2.1 million Java method-comment pairs and show improvement over four baseline techniques, two from the software engineering literature, and two from machine learning literature.
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Cites
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Gu, J., Lu, Z., Li, H., & Li, V. O. K. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1154 · Cited by 1,446
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,264
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Towards automatically generating summary comments for Java methods
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Cited by
Semantic similarity metrics for evaluating source code summarization
Haque, S., Eberhart, Z., Bansal, A., & McMillan, C. (2022) · Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension · doi:10.1145/3524610.3527909 · Cited by 116
Self-citationReassessing automatic evaluation metrics for code summarization tasks
Roy, D., Fakhoury, S., & Arnaoudova, V. (2021) · Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3468264.3468588 · Cited by 106
(2023) · doi:10.1145/3510003.3510060 · Cited by 88
Multilingual training for software engineering
Ahmed, T., & Devanbu, P. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510049 · Cited by 57
Correlating Automated and Human Evaluation of Code Documentation Generation Quality
Hu, X., Chen, Q., Wang, H., Xia, X., Lo, D., & Zimmermann, T. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3502853 · Cited by 33
Metadata
- Authors
- LeClair, A., Haque, S., Wu, L., & McMillan, C.
- Venue
- Proceedings of the 28th International Conference on Program Comprehension
- Publisher
- ACM
- Published
- 2020-07-13
- Pages
- 184-195
- OpenAlex
- W3014173464
- Cited by
- 312
- References
- 45
- FWCI
- 41.47
- Type
- conference paper
- Language
- en
- Open access
- green
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- LeClair, A., Haque, S., Wu, L., & McMillan, C. (2020). Improved Code Summarization via a Graph Neural Network. Proceedings of the 28th International Conference on Program Comprehension. https://doi.org/10.1145/3387904.3389268
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