ADI equips AI debugging agents with function-level interaction via a new execution trace structure, raising SWE-bench Verified resolution to 63.8% at $1.28 per task and delivering 6-18% gains when added to existing agents.
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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.
SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
TestGeneralizer generalizes an initial test into a set of executable tests covering more diverse scenarios, delivering +31.66% mutation-based and +23.08% LLM-assessed scenario coverage gains over ChatTester on 12 open-source Java projects.
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.
IntentionTest generates project-specific tests from a validation-intention description by retrieving a similar existing test and editing it with an LLM and project-specific code facts.
citing papers explorer
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Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis
ADI equips AI debugging agents with function-level interaction via a new execution trace structure, raising SWE-bench Verified resolution to 63.8% at $1.28 per task and delivering 6-18% gains when added to existing agents.
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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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Uncovering Business Logic Bugs via Semantics-Driven Unit Test Generation
SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
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Generalizing Test Cases for Comprehensive Test Scenario Coverage
TestGeneralizer generalizes an initial test into a set of executable tests covering more diverse scenarios, delivering +31.66% mutation-based and +23.08% LLM-assessed scenario coverage gains over ChatTester on 12 open-source Java projects.
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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.
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Generating Project-Specific Test Cases with Requirement Validation Intention
IntentionTest generates project-specific tests from a validation-intention description by retrieving a similar existing test and editing it with an LLM and project-specific code facts.