# Learning Syntactic Program Transformations from Examples

arXiv: https://arxiv.org/abs/1608.09000 · HTML: https://mlsanity.com/paper/1608.09000/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 17 of 17)

- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [On Multi-Modal Learning of Editing Source Code](https://mlsanity.com/paper/2108.06645/) (2021)
- [GrACE: Generation using Associated Code Edits](https://mlsanity.com/paper/2305.14129/) (2023)
- [Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions](https://mlsanity.com/paper/2304.03816/) (2023)
- [Neurosymbolic Repair for Low-Code Formula Languages](https://mlsanity.com/paper/2207.11765/) (2022)
- [Overwatch: Learning Patterns in Code Edit Sequences](https://mlsanity.com/paper/2207.12456/) (2022)
- [Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization](https://mlsanity.com/paper/2502.07786/) (2024)
- [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://mlsanity.com/paper/2404.04728/) (2024)
- [C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments](https://mlsanity.com/paper/2206.08768/) (2022)
- [MELT: Mining Effective Lightweight Transformations from Pull Requests](https://mlsanity.com/paper/2308.14687/) (2023)
- [MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions](https://mlsanity.com/paper/2504.09474/) (2025)
- [Evaluating Language Models on Cross-Language Code Functional Equivalence](https://mlsanity.com/paper/2608.23961/) (2026)
- [Example-based Synthesis of Static Analysis Rules](https://mlsanity.com/paper/2204.08643/) (2022)
- [Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments](https://mlsanity.com/paper/2404.01754/) (2024)
- [Designing and Evaluating Chain-of-Hints for Scientific Question Answering](https://mlsanity.com/paper/2510.21087/) (2025)
- [Agentic Generation of AST Transformation Rules for Fixing Breaking Updates](https://mlsanity.com/paper/2606.24446/) (2026)

## Abstract (arXiv)

IDEs, such as Visual Studio, automate common transformations, such as Rename and Extract Method refactorings. However, extending these catalogs of transformations is complex and time-consuming. A similar phenomenon appears in intelligent tutoring systems where instructors have to write cumbersome code transformations that describe "common faults" to fix similar student submissions to programming assignments. We present REFAZER, a technique for automatically generating program transformations. REFAZER builds on the observation that code edits performed by developers can be used as examples for learning transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of edits used by students to fix incorrect programming assignment submissions, we learn transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 59 scenarios of repetitive edits taken from 3 C# open-source projects, REFAZER learns the intended program transformation in 83% of the cases.