PBT-Bench is a new benchmark of 100 property-based testing problems with 365 injected semantic bugs across 40 Python libraries that measures LLMs on deriving invariants and precise input-generation strategies.
Can large language models write good property-based tests?
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
VeriContest supplies 946 problems with specs, code, proofs, and tests to benchmark verifiable code generation in Rust/Verus, showing models reach 92% on code but only 5% end-to-end on full verifiable synthesis.
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.
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.
citing papers explorer
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PBT-Bench: Benchmarking AI Agents on Property-Based Testing
PBT-Bench is a new benchmark of 100 property-based testing problems with 365 injected semantic bugs across 40 Python libraries that measures LLMs on deriving invariants and precise input-generation strategies.
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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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VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
VeriContest supplies 946 problems with specs, code, proofs, and tests to benchmark verifiable code generation in Rust/Verus, showing models reach 92% on code but only 5% end-to-end on full verifiable synthesis.
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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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Enhancing Program Repair with Specification Guidance and Intermediate Behavioral Signals
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
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Decision-Oriented Programming with Aporia
Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
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Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.