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Generating Random Structurally Rich Algebraic Data Type Values

机译:生成随机的结构丰富的代数数据类型值

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Automatic generation of random values described by algebraic data types (ADTs) is often a hard task. State-of-the-art random testing tools can automatically synthesize random data generators based on ADTs definitions. In that manner, generated values comply with the structure described by ADTs, something that proves useful when testing software which expects complex inputs. However, it sometimes becomes necessary to generate structural richer ADTs values in order to test deeper software layers. In this work we propose to leverage static information found in the codebase as a manner to improve the generation process. Namely, our generators are capable of considering how programs branch on input data as well as how ADTs values are built via interfaces. We implement a tool, responsible for synthesizing generators for ADTs values while providing compile-time guarantees about their distributions. Using compile-time predictions, we provide a heuristic that tries to adjust the distribution of generators to what developers might want. We report on preliminary experiments where our approach shows encouraging results.
机译:自动生成由代数数据类型(ADT)描述的随机值通常是一项艰巨的任务。先进的随机测试工具可以根据ADT定义自动合成随机数据生成器。以这种方式,生成的值符合ADT描述的结构,这在测试需要复杂输入的软件时证明是有用的。但是,有时有必要生成结构更丰富的ADT值,以测试更深的软件层。在这项工作中,我们建议利用在代码库中找到的静态信息来改善生成过程。即,我们的生成器能够考虑程序如何在输入数据上分支以及如何通过接口构建ADT值。我们实现了一个工具,负责综合生成ADT值的生成器,同时提供有关其分布的编译时保证。使用编译时预测,我们提供了一种试探法,尝试将生成器的分布调整为开发人员可能想要的。我们报告了一些初步实验,这些实验显示出令人鼓舞的结果。

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