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Generating Constrained Random Data with Uniform Distribution

机译:生成具有均匀分布的约束随机数据

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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:我们提出了一种从表示为布尔函数的谓词自动推导测试数据生成器的技术。这些生成器的分布在给定大小的值上是均匀的。为了提高生成效率,我们依赖谓词的惰性,从而使我们能够快速修剪价值空间。相反,手动实现测试数据生成器需要大量劳动并且容易出错。而且,手写生成器通常具有不可预测的值分布,冒着某些值被任意低估的风险。我们还介绍了该技术的一种变体,其中以有限且可预测的方式歪曲了分布,从而潜在地提高了性能。对这些技术的实验评估表明,统一派生的生成器比手写生成器更容易定义,并且它们的性能虽然较低,但对于某些实际应用来说是足够的。

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