初学者的幸运:一种基于属性的生成器语言

Leonidas Lampropoulos, Diane Gallois-Wong, Catalin Hritcu, John Hughes, B. Pierce, Li-yao Xia
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引用次数: 44

摘要

基于属性的随机测试需要为满足复杂逻辑谓词的分布良好的随机数据构建高效的生成器,但编写这些生成器可能很困难且容易出错。我们提出了一种特定于领域的语言,在这种语言中,通过使用轻量级注释修饰谓词来方便地表达生成器,以控制生成值的分布和每个变量实例化之前发生的约束求解量。这种称为Luck的语言使生成器更容易编写、阅读和维护。我们给出了Luck的一个形式语义,并证明了它的几个基本性质,包括关于标准谓词语义的随机生成的健全性和完备性。我们在基于属性的测试文献中的常见示例和两个重要的案例研究中对Luck进行了评估,结果表明,与手写生成器相比,它可以在复杂的领域中使用,具有相当的bug发现效率,并且显著减少了测试代码的大小。
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Beginner's luck: a language for property-based generators
Property-based random testing à 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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