---
title: "Structural Language Models of Code — paper by Alon et al. (2019)"
description: "The paper proposes structural language modeling (SLM). 45 references, cited by 18 papers here."
authors: ["Uri Alon", "Roy Sadaka", "Omer Levy", "Eran Yahav"]
year: 2019
venue: "arXiv:1910.00577"
source: "https://arxiv.org/abs/1910.00577"
doi: "10.48550/arxiv.1910.00577"
arxiv: "1910.00577"
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/alon-2019-structural-language-models-code/"
---
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# Structural Language Models of Code

**Authors:** [Uri Alon](https://md.research.elamur.ai/free/en/authors/uri-alon/), Roy Sadaka, [Omer Levy](https://md.research.elamur.ai/free/en/authors/omer-levy/) and [Eran Yahav](https://md.research.elamur.ai/free/en/authors/eran-yahav/) · **Year:** 2019 · **Venue:** arXiv:1910.00577 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1910.00577) · [PDF](https://arxiv.org/pdf/1910.00577) · [DOI 10.48550/arxiv.1910.00577](https://doi.org/10.48550/arxiv.1910.00577) · [arXiv 1910.00577](https://arxiv.org/abs/1910.00577) · [OpenAlex](https://openalex.org/W3035300716)

**Citations:** 37 citations (OpenAlex, 2026-10-14) · [18](https://md.research.elamur.ai/free/en/papers/alon-2019-structural-language-models-code/cited-by/) in our library, of the 150,927 papers whose bibliographies we read

## Summary
The authors address the task of any-code completion: a missing fragment of a program must be reconstructed from the rest of its code, with no restriction on the vocabulary or syntactic form of the result. Earlier approaches either worked with narrowly specialized languages or generated only simple expressions without user-defined functions. The paper proposes structural language modeling (SLM). A program is represented as an abstract syntax tree (AST), and its probability is decomposed by the chain rule into a product of conditional probabilities of individual nodes, ordered by a depth-first traversal. The neural network predicts each next node from a set of partial paths in the tree: from every already known leaf, and from the root, to the node currently being expanded. To control the number of child nodes and the length of identifiers, auxiliary nodes EOS_node and EOS_tok are added. The model describes the context and the missing fragment jointly, without a separate encoder and decoder. On Java and C#, SLM outperforms seq2seq and a number of structural models. The paper shows that explicitly accounting for the syntax of the language improves the generation of arbitrary code.

## Key points
- **Java, any-code completion:** exact match on the first attempt (acc@1) is 18.04%, and among five candidates (acc@5) it is 24.83%. The previous best result was 16.93% and 23.17%.
- **C#, the restricted task of Brockschmidt et al. (2019):** acc@1 is 37.61%, compared with 26.42% for previous models.
- **Node-by-node generation instead of grammar rules:** the model first decides that there will be a function call here, and only then chooses the object and the method. In production-based approaches, the method name has to be chosen before the object on which it is called.
- **Technical details:** identifiers are split into subtokens by camelCase. Paths are encoded by an LSTM, and shared path prefixes are computed once and cached, which noticeably speeds up training and inference. An ablation study showed that jointly modeling the context and the target works better than a separate encoder and decoder.
- **Resources:** the code, data and trained models are published at github.com/tech-srl/slm-code-generation, and an online demo is available at AnyCodeGen.org.

*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 Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 422 works · 213,644 citations
- [Technion – Israel Institute of Technology](https://md.research.elamur.ai/free/en/universities/technion-israel-institute-of-technology/) — University, Haifa, Israel · 59 works · 7,598 citations

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## Cite

Cite as: Uri Alon, Roy Sadaka, Omer Levy and Eran Yahav. 2019. Structural Language Models of Code. arXiv:1910.00577.

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

```bibtex
@misc{alon2019structural,
  title = {{Structural Language Models of Code}},
  author = {Uri Alon, Roy Sadaka, Omer Levy and Eran Yahav},
  year = {2019},
  doi = {10.48550/arxiv.1910.00577},
  eprint = {1910.00577},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1910.00577}
}
```

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## References

45 references, 22 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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*Updated 3 November 2026*