The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A statistical semantic language model for source code
Abstract
Recent research has successfully applied the statistical n-gram language model to show that source code exhibits a good level of repetition. The n-gram model is shown to have good predictability in supporting code suggestion and completion. However, the state-of-the-art n-gram approach to capture source code regularities/patterns is based only on the lexical information in a local context of the code units. To improve predictability, we introduce SLAMC, a novel statistical semantic language model for source code. It incorporates semantic information into code tokens and models the regularities/patterns of such semantic annotations, called sememes, rather than their lexemes. It combines the local context in semantic n-grams with the global technical concerns/functionality into an n-gram topic model, together with pairwise associations of program elements. Based on SLAMC, we developed a new code suggestion method, which is empirically evaluated on several projects to have relatively 18-68% higher accuracy than the state-of-the-art approach.
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Citations
Cites
Using task context to improve programmer productivity
Kersten, M., & Murphy, G. C. (2006) · Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1181775.1181777 · Cited by 452
How Program History Can Improve Code Completion
Robbes, R., & Lanza, M. (2008) · 2008 23rd IEEE/ACM International Conference on Automated Software Engineering · doi:10.1109/ase.2008.42 · Cited by 155 · cited 1×
“Robbes and Lanza [19] introduced six strategies to improve code completion using recent histories of modified/inserted code during an editing session and on the methods and class hierarchy related to the current variable.”
Background
Cited by
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
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
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · arXiv · doi:10.48550/arxiv.1704.01696 · Cited by 585
IntelliCode compose: code generation using transformer
Svyatkovskiy, A., Deng, S. K., Fu, S., & Sundaresan, N. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3417058 · Cited by 474
“Some of the early applications of machine learning of source code include code search [1, 2], bug detection and localization [3], program synthesis [4], code summarization [5] and code completion [6–10].”
BackgroundOn the naturalness of software
Hindle, A., Barr, E. T., Gabel, M., Su, Z., & Devanbu, P. (2016) · Communications of the ACM · doi:10.1145/2902362 · Cited by 307
Metadata
- Authors
- Nguyen, T. T., Nguyen, A. T., Nguyen, H. A., & Nguyen, T. N.
- Venue
- Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2013-08-18
- Pages
- 532-542
- OpenAlex
- W1994573369
- Cited by
- 208
- References
- 2
- FWCI
- 15.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
