The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Summarizing Source Code using a Neural Attention Model
Open accessAbstract
High quality source code is often paired with high level summaries of the computation it performs, for example in code documentation or in descriptions posted in online forums.Such summaries are extremely useful for applications such as code search but are expensive to manually author, hence only done for a small fraction of all code that is produced.In this paper, we present the first completely datadriven approach for generating high level summaries of source code.Our model, CODE-NN , uses Long Short Term Memory (LSTM) networks with attention to produce sentences that describe C# code snippets and SQL queries.CODE-NN is trained on a new corpus that is automatically collected from StackOverflow, which we release.Experiments demonstrate strong performance on two tasks: (1) code summarization, where we establish the first end-to-end learning results and outperform strong baselines, and (2) code retrieval, where our learned model improves the state of the art on a recently introduced C# benchmark by a large margin.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Moses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735
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
Chinese Poetry Generation with Recurrent Neural Networks
Zhang, X., & Lapata, M. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1074 · Cited by 298
(2004) · doi:10.3115/1220355.1220427 · Cited by 294
Supporting program comprehension with source code summarization
Haiduc, S., Aponte, J., & Marcus, A. (2010) · Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2 · doi:10.1145/1810295.1810335 · Cited by 291
Cited by
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.139 · Cited by 2,824
code2vec: Learning distributed representations of code
Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3290353 · Cited by 962
Large Language Models for Software Engineering: A Systematic Literature Review
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3695988 · Cited by 1,029
Learning to Ask: Neural Question Generation for Reading Comprehension
Du, X., Shao, J., & Cardie, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1123 · Cited by 696
“…inspired by the recent success in neural machine translation (Sutskever et al., 2014; Bahdanau et al., 2015), summarization (Rush et al., 2015; Iyer et al., 2016), and image caption generation (Xu et al., 2015), we tackle question generation using a conditional neural language model with a…”
BackgroundMethodUnified Pre-training for Program Understanding and Generation
Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642
Metadata
- Authors
- Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L.
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 2073-2083
- OpenAlex
- W2516621648
- Cited by
- 701
- References
- 10
- FWCI
- 29.06
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L. (2016). Summarizing Source Code using a Neural Attention Model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p16-1195
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