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Generating Constrained Random Data with Uniform Distribution

机译:使用均匀分布生成受限的随机数据

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We present a technique for automatically deriving test data generators from a predicate expressed as a Boolean function. The distribution of these generators is uniform over values of a given size. To make the generation efficient we rely on laziness of the predicate, allowing us to prune the space of values quickly. In contrast, implementing test data generators by hand is labour intensive and error prone. Moreover, handwritten generators often have an unpredictable distribution of values, risking that some values are arbitrarily underrepresented. We also present a variation of the technique where the distribution is skewed in a limited and predictable way, potentially increasing the performance. Experimental evaluation of the techniques shows that the uniform derived generators are much easier to define than hand-written ones, and their performance, while lower, is adequate for some realistic applications.
机译:我们介绍了一种用于从表示为布尔函数的谓词自动导出测试数据生成器的技术。这些发生器的分布在给定尺寸的值均匀。使得发电效率依靠谓词的懒惰,允许我们快速地修剪价值观。相比之下,通过手动实施测试数据发生器是劳动密集型和易于错误的。此外,手写的发电机通常具有不可预测的值分布,风险有些值是任意强度的。我们还提出了一种以有限且可预测的方式歪斜分布的技术的变化,可能会增加性能。该技术的实验评估表明,均匀的导出发生器比手写的更容易定义,而且它们的性能在较低的情况下足以实现一些现实应用。

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