Property-based random testingà la QuickCheck requires building efficient generators for well-distributed random data satisfying complex logical predicates, but writing these generators can be difficult and error prone. We propose a domain-specific language in which generators are conveniently expressed by decorating predicates with lightweight annotations to control both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. This language, called Luck, makes generators easier to write, read, and maintain.We give Luck a formal semantics and prove several fundamental properties, including the soundness and completeness of random generation with respect to a standard predicate semantics. We evaluate Luck on common examples from the property-based testing literature and on two significant case studies, showing that it can be used in complex domains with comparable bug-finding effectiveness and a significant reduction in testing code size compared to handwritten generators.

We demonstrate for the first time that Liquid Haskell, a refinement type checker for Haskell programs, can be used for arbitrary theorem proving by verifying a parallel, monoidal string matching algorithm implemented in Haskell. We use refinement types to specify correctness properties, Haskell terms to express proofs of these properties, and Liquid Haskell to check the proofs. We evaluate Liquid Haskell as a theorem prover by replicating our 1428 LoC proof in a dependently-typed language (Coq - 1136 LoC). Finally, we compare both proofs, uncovering the relative advantages and disadvantages of the two provers.