How much testing is enough? Applying stopping rules to behavioral model testing

Tom Chen, A. Andrews, A. Hajjar, Charles Anderson, M. Sahinoglu
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引用次数: 18

Abstract

Testing behavioral models before they are released to the synthesis and logic design phase is a tedious process, to say the least. A common practice is the test-it-to-death approach in which millions or even billions of vectors are applied and the results are checked for possible bugs. The vectors applied to behavioral models include functional vectors, but the significant amount of the vectors are random in nature, including random combinations of instructions. In this paper, we present and evaluate a stopping rule that can be used to determine when to stop the current testing phase using a given testing technique. We demonstrate the use of the stopping rule on two complex VHDL models that were tested for branch coverage with 4 different testing phases. We compare savings and quality of testing both with and without using the stopping rule.
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多少测试才足够?在行为模型测试中应用停止规则
至少可以这么说,在将行为模型发布到合成和逻辑设计阶段之前对它们进行测试是一个乏味的过程。一种常见的做法是“从测试到死亡”的方法,在这种方法中,应用数百万甚至数十亿个向量,并检查结果是否存在可能的bug。应用于行为模型的向量包括功能向量,但大量的向量本质上是随机的,包括指令的随机组合。在本文中,我们提出并评估了一个停止规则,该规则可用于使用给定的测试技术确定何时停止当前测试阶段。我们在两个复杂的VHDL模型上演示了停止规则的使用,这些模型通过4个不同的测试阶段测试了分支覆盖率。我们比较了使用和不使用停止规则的测试的节省和质量。
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