LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops
Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Researchers and developers often face wasted effort in implementing checks and refining LLM-generated code, frequently duplicating their efforts. This paper presents LLMLOOP, a framework that automates the refinement of both source code and test cases produced by LLMs. LLMLOOP employs five iterative loops: resolving compilation errors, addressing static analysis issues, fixing test case failures, and improving test quality through mutation analysis. These loops ensure the generation of high-quality test cases that ser...
| Authors | Ravin Ravi, Dylan Bradshaw, Stefano Ruberto et al. |
|---|---|
| Primary category | cs.SE |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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