Pythia: AI-assisted Code Completion System
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Traditional code completion techniques typically involve analyzing code to identify potential suggestions, followed by re-ranking them.
“Traditional code completion techniques typically involve analyzing code to identify potential suggestions, followed by re-ranking them”
From RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023) · cites this paper as Svyatkovskiy et al., 2019
The traditional re-ranking approach offers efficient performance but lacks flexibility to generate code at arbitrary granularity.
“While this approach offers efficient performance, it lacks the flexibility to generate code at arbitrary granularity.”
From RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023) · cites this paper as Svyatkovskiy et al., 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 18
- G. C. Murphy, M. Kersten, and L. Findlater. How are java software developers using the eclipse ide? IEEE, 23(4):76-83, 2006.
- Sebastian Proksch, Johannes Lerch, and Mira Mezini. Intelligent code completion with bayesian networks. ACM Transactions on Software Engineering and Methodology (TOSEM), 25(1):3, 2015.
- D'Souza Andrea Renika, Di Yang, and Cristina V Lopes. Collective intelligence for smarter api recommendations in python. In Source Code Analysis and Manipulation (SCAM), 2016 IEEE 16th International Working Conference on, pages 51-60. IEEE, 2016.
- Veselin Raychev, Martin Vechev, and Eran Yahav. Code completion with statistical language models. In ACM SIGPLAN Notices, volume 49, pages 419-428. ACM, 2014.
- Marcel Bruch, Martin Monperrus, and Mira Mezini. Learning from examples to improve code completion systems. In Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, pages 213-222. ACM, 2009.
- A. Alnusair, T. Zhao, and E. Bodden. Effective api navigation and reuse. IRI, pages 7-12, 2010.
- T. Gvero, V. Kuncak, I. Kuraj, and R. Piskac. Complete completion using types and weights. In PLDI'13. ACM, 2013.
- A. Zimdars, D. M. Chickering, and C. Meek. Using temporal data for making recommendations. In Proceedings of 7th Conference on Uncertainty in Artificial Intelligence, pages 580-588. UAI, 2001.
- B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. In International Conference on Data Mining. IEEE, 2002.
- R. I. Brafman G. Shani and D. Heckerman. An mdp-based recommender system. In Proceedings of 8th Conference on Uncertainty in Artificial Intelligence. UAI, 2002.
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111-3119.
- Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan Yuille. Deep captioning with multimodal recurrent neural networks (m-rnn). ICLR, 2015.
- Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Stroudsburg, PA, October 2013. Association for Computational Linguistics.
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3104-3112.
- Hakan Inan, Khashayar Khosravi, and Richard Socher. Tying word vectors and word classifiers: A loss framework for language modeling. CoRR, abs/1611.01462, 2016. · arXiv
- Alex Graves. Generating sequences with recurrent neural networks. CoRR, abs/1308.0850, 2013. · arXiv
- Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: training imagenet in 1 hour. CoRR, abs/1706.02677, 2017. · arXiv
- Colin Raffel and Daniel P. W. Ellis. Feed-forward networks with attention can solve some long-term memory problems. CoRR, abs/1512.08756, 2015. · arXiv
Cited by
Sorted by citations
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 · see citing works
“There are numerous code completion systems capable of effectively recommending method and API calls [6, 9–11], or finding the correct argument [12–14].”
MethodSelf-citationWILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
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 · see citing works
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Zhang, F., Chen, B., Zhang, Y., Keung, J., Liu, J., Zan, D., Mao, Y., Lou, J.-G., & Chen, W. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.151 · Cited by 173 · see citing works
AI-driven innovations in software engineering: a review of current practices and future directions
Alenezi, M., & Akour, M. (2025) · Applied Sciences · doi:10.3390/app15031344 · Cited by 121 · see citing works
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 · see citing works
Self-citationSignificant Productivity Gains through Programming with Large Language Models
Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3661145 · Cited by 64 · see citing works
“…and industry have explored different mechanisms how these suggestions and code completions can be generated, from semantic analysis of the source code [3, 22, 28, 48, 49] to machine learning approaches [43, 57] Programming with Large Language Models 256:3 and different combinations [7, 47].”
Ulfsnes, R., Moe, N. B., Stray, V., & Skarpen, M. (2024) · Generative AI for Effective Software Development · doi:10.1007/978-3-031-55642-5_10 · Cited by 62 · see citing works
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Clement, C., Lu, S., Liu, X., Tufano, M., Drain, D., Duan, N., Sundaresan, N., & Svyatkovskiy, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.387 · Cited by 12 · see citing works
Self-citationCodeCleaner: Mitigating Data Contamination for LLM Benchmarking
Cao, J., Chen, S., Zhang, W., Lo, H. C., Li, Y., & Cheung, S.-C. (2025) · Proceedings of the 16th International Conference on Internetware · doi:10.1145/3755881.3755901 · Cited by 2 · see citing works
- Authors
- Svyatkovskiy, A., Zhao, Y., Fu, S., & Sundaresan, N.
- Affiliations
- Microsoft, Redmond, WA, USA
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 2727-2735
- Cited by
- 125
- References
- 0
- Funders
- Microsoft
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
