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Predicting Program Properties from "Big Code"
Abstract
We present a new approach for predicting program properties from massive codebases (aka "Big Code"). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs.
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Citations
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
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
On 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
The adverse effects of code duplication in machine learning models of code
Allamanis, M. (2019) · Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software · doi:10.1145/3359591.3359735 · Cited by 284
Learning to mine aligned code and natural language pairs from stack overflow
Yin, P., Deng, B., Chen, E., Vasilescu, B., & Neubig, G. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196408 · Cited by 218
Metadata
- Authors
- Raychev, V., Vechev, M., & Krause, A.
- Venue
- Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
- Publisher
- ACM
- Published
- 2015-01-14
- Pages
- 111-124
- OpenAlex
- W2156981320
- Cited by
- 296
- References
- 0
- FWCI
- 25.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Predicting Program Properties from "Big Code". https://app.cochat.ai/papers/10.1145/2676726.2677009
- The paper
- Raychev, V., Vechev, M., & Krause, A. (2015). Predicting Program Properties from "Big Code". Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. https://doi.org/10.1145/2676726.2677009
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.
