Making Our Own Luck A Language for Random Generators ( Extended Abstract )

QuickCheck-style property-based random testing [4] requires efficient generators for well-distributed random data satisfying complex logical predicates. Writing such generators by hand can be difficult and error prone. We propose a domain-specific language, Luck, in which generators are expressed by decorating predicates with lightweight annotations controlling both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. Generators in Luck are compact, readable, and maintainable, with efficiency close to custom handwritten generators. We give a precise semantics for Luck, reminiscent of those for probabilistic languages [7], and prove key theorems about its behavior, including the soundness and completeness of random generation with respect to a straightforward predicate semantics.