SwePub - search: WFRF:(Lampropoulos Leonidas)

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1.	Lampropoulos, Leonidas, et al. (author) Beginner's luck: a language for property-based generators 2017 In: SIGPLAN Notices (ACM Special Interest Group on Programming Languages). - New York, NY, USA : ACM. - 0730-8566. ; 52:1, s. 114-129 Conference paper (peer-reviewed)abstract Property-based random testing a 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.

Lampropoulos, Leonidas, et al. (author)
Beginner's luck: a language for property-based generators
2017
In: SIGPLAN Notices (ACM Special Interest Group on Programming Languages). - New York, NY, USA : ACM. - 0730-8566. ; 52:1, s. 114-129
Conference paper (peer-reviewed)abstract
- Property-based random testing a 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.

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