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Mining source code repositories at massive scale using language modeling
Open accessAbstract
The tens of thousands of high-quality open source software projects on the Internet raise the exciting possibility of studying software development by finding patterns across truly large source code repositories. This could enable new tools for developing code, encouraging reuse, and navigating large projects. In this paper, we build the first giga-token probabilistic language model of source code, based on 352 million lines of Java. This is 100 times the scale of the pioneering work by Hindle et al. The giga-token model is significantly better at the code suggestion task than previous models. More broadly, our approach provides a new “lens” for analyzing software projects, enabling new complexity metrics based on statistical analysis of large corpora. We call these metrics data-driven complexity metrics. We propose new metrics that measure the complexity of a code module and the topical centrality of a module to a software project. In particular, it is possible to distinguish reusable utility classes from classes that are part of a program's core logic based solely on general information theoretic criteria.
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Citations
Cited by
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.499 · Cited by 673
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.04664 · Cited by 536
Program Synthesis with Large Language Models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07732 · Cited by 388
“Early work applied statistical n-gram models [Hindle et al., 2012, Allamanis and Sutton, 2013a] and neural networks [Maddison and Tarlow, 2014, Raychev et al., 2014] to code.”
MethodSelf-citationCurating GitHub for engineered software projects
Munaiah, N., Kroh, S., Cabrey, C., & Nagappan, M. (2017) · Empirical Software Engineering · doi:10.1007/s10664-017-9512-6 · Cited by 365
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
Metadata
- Authors
- Allamanis, M., & Sutton, C.
- Venue
- 2013 10th Working Conference on Mining Software Repositories (MSR)
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 207-216
- OpenAlex
- W2148190602
- Cited by
- 313
- References
- 0
- FWCI
- 15.34
- Type
- conference paper
- Language
- en
- Open access
- green
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- Allamanis, M., & Sutton, C. (2013). Mining source code repositories at massive scale using language modeling. 2013 10th Working Conference on Mining Software Repositories (MSR). https://doi.org/10.1109/msr.2013.6624029
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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.
