# MELT: Mining Effective Lightweight Transformations from Pull Requests

arXiv: https://arxiv.org/abs/2308.14687 · HTML: https://mlsanity.com/paper/2308.14687/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration](https://mlsanity.com/paper/1709.04621/) (2017)
- [Learning Syntactic Program Transformations from Examples](https://mlsanity.com/paper/1608.09000/) (2016)
- [SOAR: A Synthesis Approach for Data Science API Refactoring](https://mlsanity.com/paper/2102.06726/) (2021)
- [Vulnerable Open Source Dependencies: Counting Those That Matter](https://mlsanity.com/paper/1808.09753/) (2018)
- [IntelliTC: Automating Type Changes in IntelliJ IDEA](https://mlsanity.com/paper/2112.03619/) (2021)

## Built on by (most cited first, 2 of 2)

- [SPELL: Synthesis of Programmatic Edits using LLMs](https://mlsanity.com/paper/2602.01107/) (2026)
- [Agentic Generation of AST Transformation Rules for Fixing Breaking Updates](https://mlsanity.com/paper/2606.24446/) (2026)

## Abstract (arXiv)

Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce MELT, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositories. Our key insight is that pull requests merged into open-source libraries are a rich source of information sufficient to mine API migration rules. By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in comby, a language for structural code search and replace. Since inferred rules from single code examples may be too specific, we propose a generalization procedure to make the rules more applicable to client projects. MELT rules are syntax-driven, interpretable, and easily adaptable. Moreover, unlike previous work, our approach enables rule inference to seamlessly integrate into the library workflow, removing the need to wait for client code migrations. We evaluated MELT on pull requests from four popular libraries, successfully mining 461 migration rules from code examples in pull requests and 114 rules from auto-generated code examples. Our generalization procedure increases the number of matches for mined rules by 9x. We applied these rules to client projects and ran their tests, which led to an overall decrease in the number of warnings and fixing some test cases demonstrating MELT's effectiveness in real-world scenarios.