The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
AutoComment: Mining question and answer sites for automatic comment generation
Abstract
Code comments improve software maintainability. To address the comment scarcity issue, we propose a new automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings.We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open-source projects. We apply AutoComment to analyze Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment to generate 102 comments automatically for 23 Java and Android projects. The user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code.
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WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
Towards automatically generating summary comments for Java methods
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., & Vijay-Shanker, K. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859006 · Cited by 514
Automatically documenting program changes
Buse, R. P. L., & Weimer, W. R. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859005 · Cited by 219
Cited by
Hu, X., Li, G., Xia, X., Lo, D., & Jin, Z. (2018) · Proceedings of the 26th Conference on Program Comprehension · doi:10.1145/3196321.3196334 · Cited by 621
Measuring Program Comprehension: A Large-Scale Field Study with Professionals
Xia, X., Bao, L., Lo, D., Xing, Z., Hassan, A. E., & Li, S. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2734091 · Cited by 326
Retrieval-based neural source code summarization
Zhang, J., Wang, X., Zhang, H., Sun, H., & Liu, X. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380383 · Cited by 275
Learning to mine aligned code and natural language pairs from stack overflow
Yin, P., Deng, B., Chen, E., Vasilescu, B., & Neubig, G. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196408 · Cited by 218
“, only consider accepted answers with a single code block or with high vote counts, or filtering out print statements in Python, much like one thrust of prior work [15, 44]); fine-tuning heuristics may achieve high pair quality, but this inherently reduces the size of the mined data set and it may also be very languag…”
BackgroundMethodResultInfluentialAutomatic Code Documentation Generation Using GPT-3
Khan, J. Y., & Uddin, G. (2022) · Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3551349.3559548 · Cited by 96
Metadata
- Authors
- Wong, E., Yang, J., & Tan, L.
- Venue
- 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2013-11
- Pages
- 562-567
- OpenAlex
- W2023925487
- Cited by
- 245
- References
- 3
- FWCI
- 6.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wong, E., Yang, J., & Tan, L. (2013). AutoComment: Mining question and answer sites for automatic comment generation. 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE). https://doi.org/10.1109/ase.2013.6693113
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