PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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8 Pith papers cite this work. Polarity classification is still indexing.
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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.
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
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.
AnyPoC validates LLM bug reports by synthesizing, executing, and independently re-checking proof-of-concept tests, finding 121 new bugs with 108 confirmed across large systems.
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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From Exploration to Specification: LLM-Based Property Generation for Mobile App Testing
PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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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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Call-Chain-Aware LLM-Based Test Generation for Java Projects
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
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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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AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
AnyPoC validates LLM bug reports by synthesizing, executing, and independently re-checking proof-of-concept tests, finding 121 new bugs with 108 confirmed across large systems.
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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.
- Generating Project-Specific Test Cases with Requirement Validation Intention