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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106290 · Cited by 292
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
Code Prediction by Feeding Trees to Transformers
Kim, S., Zhao, J., Tian, Y., & Chandra, S. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) · doi:10.1109/icse43902.2021.00026 · Cited by 209
Multi-task Learning based Pre-trained Language Model for Code Completion
Liu, F., Li, G., Zhao, Y., & Jin, Z. (2020) · Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3324884.3416591 · Cited by 188
Big code != big vocabulary: open-vocabulary models for source code
Karampatsis, R.-M., Babii, H., Robbes, R., Sutton, C., & Janes, A. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380342 · Cited by 182
(2014) · doi:10.1145/2635868.2635901 · Cited by 159
Fast and Memory-Efficient Neural Code Completion
Svyatkovskiy, A., Lee, S., Hadjitofi, A., Riechert, M., Franco, J. V., & Allamanis, M. (2021) · 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00045 · Cited by 95
A Survey of Machine Learning for Big Code and Naturalness
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2019) · ACM Computing Surveys · doi:10.1145/3212695 · Cited by 150
CodeFill: multi-token code completion by jointly learning from structure and naming sequences
Izadi, M., Gismondi, R., & Gousios, G. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510172 · Cited by 86
When Code Completion Fails: A Case Study on Real-World Completions
Hellendoorn, V. J., Proksch, S., Gall, H. C., & Bacchelli, A. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00101 · Cited by 76
CSCC: Simple, Efficient, Context Sensitive Code Completion
Asaduzzaman, M., Roy, C. K., Schneider, K. A., & Hou, D. (2014) · 2014 IEEE International Conference on Software Maintenance and Evolution · doi:10.1109/icsme.2014.29 · Cited by 55
Does BLEU Score Work for Code Migration?
Tran, N., Tran, H., Nguyen, S., Nguyen, H., & Nguyen, T. (2019) · 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2019.00034 · Cited by 49
Self-citationCACHECA: A Cache Language Model Based Code Suggestion Tool
Franks, C., Tu, Z., Devanbu, P., & Hellendoorn, V. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.228 · Cited by 30
Learning Autocompletion from Real-World Datasets
Aye, G. A., Kim, S., & Li, H. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00022 · Cited by 29
Using machine translation for converting Python 2 to Python 3 code
Aggarwal, K., Salameh, M., & Hindle, A. (2015) · doi:10.7287/peerj.preprints.1459v1 · Cited by 20
Toward a Theory of Causation for Interpreting Neural Code Models
Nader Palacio, D., Velasco, A., Cooper, N., Rodriguez, A., Moran, K., & Poshyvanyk, D. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3379943 · Cited by 18
New Initiative: The Naturalness of Software
Devanbu, P. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.190 · Cited by 9
Improving Code Autocompletion with Transfer Learning
Zhou, W., Kim, S., Murali, V., & Aye, G. A. (2021) · arXiv · doi:10.48550/arxiv.2105.05991
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
