---
title: "A Survey of Machine Learning for Big Code and Naturalness (2017)"
description: "The authors (Microsoft Research, UCL, UC Davis, Edinburgh/Turing Institute) first compare… 201 references, cited by 76 papers here."
authors: ["Miltiadis Allamanis", "Earl T. Barr", "Premkumar Devanbu", "Charles Sutton"]
year: 2017
venue: "arXiv:1709.06182"
source: "https://arxiv.org/abs/1709.06182"
doi: "10.48550/arxiv.1709.06182"
arxiv: "1709.06182"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/"
---
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# A Survey of Machine Learning for Big Code and Naturalness

**Authors:** [Miltiadis Allamanis](https://md.research.elamur.ai/free/en/authors/miltiadis-allamanis/), Earl T. Barr, Premkumar Devanbu and [Charles Sutton](https://md.research.elamur.ai/free/en/authors/charles-sutton/) · **Year:** 2017 · **Venue:** [arXiv:1709.06182](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/) ([ACM Computing Surveys 2017](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/2017/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1709.06182) · [PDF](https://arxiv.org/pdf/1709.06182) · [DOI 10.48550/arxiv.1709.06182](https://doi.org/10.48550/arxiv.1709.06182) · [arXiv 1709.06182](https://arxiv.org/abs/1709.06182) · [OpenAlex](https://openalex.org/W2754629507)

**Citations:** 91 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey maps out a young field that applies machine learning to source code. It is framed by the "naturalness hypothesis": code is written by people for people, so large code corpora show statistical regularities similar to those in natural language, and tools can exploit them. The authors (Microsoft Research, UCL, UC Davis, Edinburgh/Turing Institute) first compare programming and natural languages and explain how the differences shape model design. They then organise probabilistic models of code into a taxonomy based on the design principles behind each model. The models covered include n-gram language models, deep learning approaches, and representations built on sequences, trees and continuous embeddings, as opposed to simple bag-of-words views. Next, they survey where these models are used, including tools that make recommendations to developers, finding and fixing bugs, analysing programs, and automatically generating programs, and then discuss open challenges. The main argument is that software engineering and programming languages research should complement purely formal, worst-case reasoning with data-driven models tuned to the common case. NLP went through a similar shift away from rule-based systems.

## Key points
- Hindle et al. provided the first empirical support for naturalness, showing that models built for natural language worked surprisingly well on code.
- "Big code" refers to the open-source data now available, which amounts to billions of code tokens plus millions of commits, bug fixes and reviews.
- Table 4 lists 12 public datasets. Examples include a corpus of 150k Python functions with docstrings (Barone and Sennrich), 800k+ repositories (Dyer et al.), JavaScript/Python AST collections (Raychev et al.), and Python code paired with English/Japanese pseudocode (Oda et al.).
- The authors maintain living resources at learnbigcode.github.io and ml4code.github.io, which accept community contributions.
- Related fields include mining software repositories (MSR) and probabilistic programming. The authors describe probabilistic programming as the "dual" direction, since it uses PL concepts to help write ML, while this field uses ML to help write programs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 381 works · 80,747 citations
- [University College London](https://md.research.elamur.ai/free/en/universities/university-college-london/) — University, London, United Kingdom · 107 works · 22,924 citations
- [The Alan Turing Institute](https://md.research.elamur.ai/free/en/labs/the-alan-turing-institute/) — Research institute, London, United Kingdom · 31 works · 4,535 citations
- [University of California, Davis](https://md.research.elamur.ai/free/en/universities/university-of-california-davis/) — University, Davis, United States · 34 works · 42,940 citations
- [Microsoft Research (United Kingdom)](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) — Company, Cambridge, United Kingdom · 11 works · 3,215 citations

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## Cite

Cite as: Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu and Charles Sutton. 2017. A Survey of Machine Learning for Big Code and Naturalness. arXiv:1709.06182.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{allamanis2017survey,
  title = {{A Survey of Machine Learning for Big Code and Naturalness}},
  author = {Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu and Charles Sutton},
  year = {2017},
  doi = {10.48550/arxiv.1709.06182},
  eprint = {1709.06182},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1709.06182}
}
```

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## References

201 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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3. Alex A Alemi, Francois Chollet, Geoffrey Irving, Christian Szegedy, and Josef Urban. 2016. [DeepMath–Deep Sequence Models for Premise Selection](https://md.research.elamur.ai/free/en/papers/alemi-2016-deepmath-deep-sequence-models-premise/). In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS).
4. Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar Devanbu, Mark Marron, and Charles Sutton. 2016. Mining Semantic Loop Idioms from Big Code. Technical Report. [https://www.microsoft.com/en-us/research/publication/mining-semantic-loop-idioms-big-code/](https://www.microsoft.com/en-us/research/publication/mining-semantic-loop-idioms-big-code/) [↗](https://www.microsoft.com/en-us/research/publication/mining-semantic-loop-idioms-big-code/)
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10. Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016. [A Convolutional Attention Network for Extreme Summarization of Source Code](https://md.research.elamur.ai/free/en/papers/allamanis-2016-convolutional-attention-network-extreme-summarization/). In Proceedings of the International Conference on Machine Learning (ICML).
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*Updated 2 November 2026*