Underapproximate types with symbolic traces guide synthesis of test generators that outperform defaults in property-based testing and model checking for effectful programs.
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A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
ContractEval benchmark on 364 tasks shows code LLMs achieve 75-82% functional pass@1 but 0% contract satisfaction under standard prompting, rising only to 23-41% with explicit contracts.
Allegro applies multi-stage programming to PBT generators and pairs it with faster randomness to achieve up to 13x faster bug discovery while exactly preserving generator semantics.
A symbolic protocol operationalizes Peirce's tripartite reasoning for LLMs using five algebraic invariants including a Weakest Link bound to enforce logical consistency and prevent weak premises from supporting strong conclusions.
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.
citing papers explorer
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Trace-Guided Synthesis of Effectful Test Generators
Underapproximate types with symbolic traces guide synthesis of test generators that outperform defaults in property-based testing and model checking for effectful programs.
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The Search for Constrained Random Generators
A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
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Fail Faster: Staging and Fast Randomness for High-Performance PBT
Allegro applies multi-stage programming to PBT generators and pairs it with faster randomness to achieve up to 13x faster bug discovery while exactly preserving generator semantics.