Code Transformation Rule Synthesis using LLMs: Potential and Limits

• URL: http://arxiv.org/abs/2609.03592v1
• Date: Thu, 03 Sep 2026 09:40:30 GMT
• Title: Code Transformation Rule Synthesis using LLMs: Potential and Limits
• Abstract summary: This study targets three languages for transformation rules, namely Comby, GritQL, and Ast-Grep.<n>We evaluate three LLMs (GPT-5.4, GPT-oss-120B, and Llama3.1-8B) on six diverse datasets covering four software- evolution tasks.<n>GPT-5.4 achieves consistently high rule applicability rates and produces transformations closest to the ground truth.<n>Smaller and open-weight GPT-oss-120B and Llama3.1-8B models remain effective for simpler, localized changes but struggle with complex migration scenarios.
• Score: 3.8596904019882206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to their black-box nature, LLMs suffer from limited explain- ability and a lack of determinism. Their usage cost can also rise, particularly with repetitive tasks on large codebases. To mitigate this, we conduct a novel empirical study targeting three domain- specific languages for transformation rules, namely Comby, GritQL, and Ast-Grep. We evaluate three LLMs (GPT-5.4, GPT-oss-120B, and Llama3.1-8B) on six diverse datasets covering four software- evolution tasks: API misuse correction, program repair, API migra- tion, and language version migration. Our results provide evidence that transformation rule synthesis moves beyond proof-of-concept with strong frontier models. GPT-5.4 achieves consistently high rule applicability rates and produces transformations closest to the ground truth across most benchmarks. Smaller and open-weight GPT-oss-120B and Llama3.1-8B models remain effective for simpler, localized changes but struggle with complex migration scenarios. We also observe non-negligible generalizability through the usage of meta-variables and through a high reuse score in the first quartile of many datasets. Finally, when compared to the anti-unification algorithm, LLMs outperform it in correctness, but underperform in rule applicability. Overall, our results show great potential for LLMs to generate sound, correct, generalizable, and reusable rules.

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