# SPELL: Synthesis of Programmatic Edits using LLMs

arXiv: https://arxiv.org/abs/2602.01107 · HTML: https://mlsanity.com/paper/2602.01107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [CAT-LM: Training Language Models on Aligned Code And Tests](https://mlsanity.com/paper/2310.01602/) (2023)
- [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)
- [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024)
- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [SOAR: A Synthesis Approach for Data Science API Refactoring](https://mlsanity.com/paper/2102.06726/) (2021)
- [IntelliTC: Automating Type Changes in IntelliJ IDEA](https://mlsanity.com/paper/2112.03619/) (2021)
- [MELT: Mining Effective Lightweight Transformations from Pull Requests](https://mlsanity.com/paper/2308.14687/) (2023)

## Built on by (most cited first, 1 of 1)

- [Agentic Generation of AST Transformation Rules for Fixing Breaking Updates](https://mlsanity.com/paper/2606.24446/) (2026)

## Abstract (arXiv)

Library migration is a common but error-prone task in software development. Developers may need to replace one library with another due to reasons like changing requirements or licensing changes. Migration typically entails updating and rewriting source code manually. While automated migration tools exist, most rely on mining examples from real-world projects that have already undergone similar migrations. However, these data are scarce, and collecting them for arbitrary pairs of libraries is difficult. Moreover, these migration tools often miss out on leveraging modern code transformation infrastructure. In this paper, we present a new approach to automated API migration that sidesteps the limitations described above. Instead of relying on existing migration data or using LLMs directly for transformation, we use LLMs to extract migration examples. Next, we use an Agent to generalize those examples to reusable transformation scripts in PolyglotPiranha, a modern code transformation tool. Our method distills latent migration knowledge from LLMs into structured, testable, and repeatable migration logic, without requiring preexisting corpora or manual engineering effort. Experimental results across Python libraries show that our system can generate diverse migration examples and synthesize transformation scripts that generalize to real-world codebases.