MIST-RL：基于突变的增量测试套件生成 via 强化学习
MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
AI总结：
MIST-RL通过强化学习优化测试生成，提升突变得分并减少测试用例数量，改进代码验证效果。
摘要：
大型语言模型（LLMs）在首次尝试中往往无法生成正确的代码，这需要使用生成的单元测试作为验证器来验证解决方案。尽管最近的验证方法取得了成功，但它们仍然受到一种
英文摘要：
Large Language Models (LLMs) often fail to generate correct code on the first attempt, which requires using generated unit tests as verifiers to validate the solutions. Despite the success of recent verification methods, they remain constrained by a "scaling-by-quantity" paradigm. This brute-force approach suffers from a critical limitation: it yields diminishing returns in fault detection while causing severe test redundancy. To address this, we propose MIST-RL (Mutation-based Incremental Suite Testing via Reinforcement Learning), a framework that shifts the focus to "scaling-by-utility". We formulate test generation as a sequential decision process optimized via Group Relative Policy Optimization (GRPO). Specifically, we introduce a novel incremental mutation reward combined with dynamic penalties, which incentivizes the model to discover new faults while it suppresses functionally equivalent assertions. Experiments on HumanEval+ and MBPP+ demonstrate that MIST-RL outperforms state-of-the-art baselines. It achieves a +28.5% higher mutation score while reducing the number of test cases by 19.3%. Furthermore, we show that these compact, high-utility tests serve as superior verifiers, which improves downstream code reranking accuracy on HumanEval+ by 3.05% over the SOTA baseline with 10 candidate samples. The source code and data are provided in the supplementary material.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
