Neural Code Summarization
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Abstract (arXiv)
Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks for knowledge transfer. As the codebase evolves over time, the description needs to be manually updated each time with the changes made. An automatic approach is proposed to infer such captions based on benchmarked and custom datasets with comparison between the original and generated results.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 26 Feb 2021.
Builds on
9 of 14 references.
References · 9
- 2017Attention Is All You Needcited by 60,361
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
- 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
- 2015Attention-Based Models for Speech Recognitioncited by 401
- 2019Neural Text Summarization: A Critical Evaluationcited by 189
- 2020Improved Code Summarization via a Graph Neural Networkcited by 79
- 2019Recommendations for Datasets for Source Code Summarizationcited by 46
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Citing papers per year: 2022: 1.
1 paper.