SeGa extracts business semantics from requirements to generate unit tests that detect 22-25 more real-world business logic bugs than prior LLM-based methods in industrial Go projects.
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TestHumanizer uses LLMs as refactoring layers on EvoSuite suites to reach 88-98% compilation rates and better readability on 350 classes from Defects4J and SF110 while preserving coverage.
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.
citing papers explorer
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Humanizing Automatically Generated Unit Test Suites with LLM-Based Refactoring
TestHumanizer uses LLMs as refactoring layers on EvoSuite suites to reach 88-98% compilation rates and better readability on 350 classes from Defects4J and SF110 while preserving coverage.
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TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.