# Mining Idioms from Source Code

arXiv: https://arxiv.org/abs/1404.0417 · HTML: https://mlsanity.com/paper/1404.0417/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2014.

## Built on by (most cited first, 19 of 19)

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- [CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences](https://mlsanity.com/paper/2202.06689/) (2022)
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- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [Large Language Models of Code Fail at Completing Code with Potential Bugs](https://mlsanity.com/paper/2306.03438/) (2023)
- [Learning to Complete Code with Sketches](https://mlsanity.com/paper/2106.10158/) (2021)
- [Neural Program Generation Modulo Static Analysis](https://mlsanity.com/paper/2111.01633/) (2021)
- [R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents](https://mlsanity.com/paper/2303.00732/) (2023)
- [Generating Planning Feedback for Open-Ended Programming Exercises with LLMs](https://mlsanity.com/paper/2504.08958/) (2025)
- [Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models](https://mlsanity.com/paper/2503.07693/) (2025)
- [Bringing Structure to Naturalness: On the Naturalness of ASTs](https://mlsanity.com/paper/2504.08234/) (2025)
- [Compiling Large Multi-Modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven Perspective](https://mlsanity.com/paper/2602.13723/) (2026)
- [Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation](https://mlsanity.com/paper/2112.01259/) (2021)
- [CodeScholar: Growing Idiomatic Code Examples](https://mlsanity.com/paper/2312.15157/) (2023)
- [On Device Agentic Operation Caches -- Classifier-Centric NL-to-Action Generation](https://mlsanity.com/paper/2609.33141/) (2026)

## Abstract (arXiv)

We present the first method for automatically mining code idioms from a corpus of previously written, idiomatic software projects. We take the view that a code idiom is a syntactic fragment that recurs across projects and has a single semantic role. Idioms may have metavariables, such as the body of a for loop. Modern IDEs commonly provide facilities for manually defining idioms and inserting them on demand, but this does not help programmers to write idiomatic code in languages or using libraries with which they are unfamiliar. We present HAGGIS, a system for mining code idioms that builds on recent advanced techniques from statistical natural language processing, namely, nonparametric Bayesian probabilistic tree substitution grammars. We apply HAGGIS to several of the most popular open source projects from GitHub. We present a wide range of evidence that the resulting idioms are semantically meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. Manual examination of the most common idioms indicate that they describe important program concepts, including object creation, exception handling, and resource management.