The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation
Open accessAbstract
Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.
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Cites
BLEU: a method for automatic evaluation of machine translation
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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
Learning Accurate, Compact, and Interpretable Tree Annotation
Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220230 · Cited by 793
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
Tu, Z., Su, Z., & Devanbu, P. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635875 · Cited by 238 · cited 1×
“…this study: MeCab to tokenize Japanese sentences [29], pialign to train word alignments [22], KenLM to train Kneser-Ney smoothed language model [30], Moses to train and generate target sentences using the PBMT model [31], and Travatar to train and generate target sentences using T2SMT models [32].”
Improving automated source code summarization via an eye-tracking study of programmers
Rodeghero, P., McMillan, C., McBurney, P. W., Bosch, N., & D'Mello, S. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568247 · Cited by 198
Cited by
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1041 · Cited by 623
Self-citationA Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · arXiv · doi:10.48550/arxiv.1704.01696 · Cited by 585
Self-citationLatent Predictor Networks for Code Generation
Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1057 · Cited by 307
Investigating Explainability of Generative AI for Code through Scenario-based Design
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., & Weisz, J. D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511119 · Cited by 219
“One example is how existing work on automatic, machine translation between human natural languages [71, 72] was applied to code.”
Background
Metadata
- Authors
- Oda, Y., Fudaba, H., Neubig, G., Hata, H., Sakti, S., Toda, T., & Nakamura, S.
- Venue
- 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2015-11
- Pages
- 574-584
- OpenAlex
- W2242083635
- Cited by
- 250
- References
- 6
- FWCI
- 16.08
- Type
- conference paper
- Language
- en
- Open access
- green
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- Oda, Y., Fudaba, H., Neubig, G., Hata, H., Sakti, S., Toda, T., & Nakamura, S. (2015). Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation. 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). https://doi.org/10.1109/ase.2015.36
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