本文讨论了如何构建一个测试数据集，以使该数据集达到用于测试的规模，并且具有期望的值分布和列间相关性。
This article discusses how to make up a test data set so that it is large enough to be useful for testing and has the expected distribution of values and correlation between columns.
另外，它的嵌入式数据相关性过滤器能够检查可变数据，并根据数据驱动加载测试需求进行测试。
In addition, its built-in data correlation filters detect variable data, as well as preparing tests for data-driven load test generation.
对于科学计算循环中出现的大多数数据相关性判定问题，使用体差不等式测试算法可以获得很好的效果。
Experimental results also show that the dependence difference inequality test algorithm works very well for most of data dependencies appeared in scientific computation programs.
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