Property-based random testing in the style of QuickCheck demands efficient generators for well-distributed random data satisfying complex logical predicates, but writing these generators can be difficult and error prone. We propose a better alternative: a domain-specific language in which generators are expressed by decorating predicates with lightweight annotations to control both the distribution of generated values as well as 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; we show 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.
Wed 18 Jan Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
|14:20 - 14:45|
|Beginner's Luck: A Language for Property-Based Generators|
Leonidas LampropoulosUniversity of Pennsylvania, Diane Gallois-WongInria Paris, ENS Paris, Cătălin HriţcuInria Paris, John HughesChalmers University of Technology, Benjamin C. PierceUniversity of Pennsylvania, Li-yao XiaENS ParisPre-print
|14:45 - 15:10|
|Exact Bayesian Inference by Symbolic Disintegration|
|15:10 - 15:35|
|Stochastic Invariants for Probabilistic Termination|
|15:35 - 16:00|
|Coupling proofs are probabilistic product programs|