Hedgehog distribution semantics is non-compositional; any compositional semantics equals sampling semantics; Hedgehog→ provides compositional semantics via arrow calculus while remaining expressive.
QuickCheck: A Lightweight Tool for Random Testing of Haskell Programs,
10 Pith papers cite this work. Polarity classification is still indexing.
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Introduces a Datalog framework modeling CRDT semantics as logic programs for automated analysis, property-based testing, and evaluation on a collaborative graph editing case study.
Underapproximate types with symbolic traces guide synthesis of test generators that outperform defaults in property-based testing and model checking for effectful programs.
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.
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.
GraphFlow is an architecture for formally verifiable visual workflows that treats diagrams as executable specs with proof-checkable contracts, backed by a pilot of 8728 runs at 97.08% completion on an early prototype without the verified core.
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Compositional Generator Equivalence (Extended Version)
Hedgehog distribution semantics is non-compositional; any compositional semantics equals sampling semantics; Hedgehog→ provides compositional semantics via arrow calculus while remaining expressive.
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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.