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TAIR:Testability analysis by implication reasoning

机译:TAIR:通过隐含推理进行可测试性分析

摘要

[[abstract]]To predict the difficulty of testing a wire stuck-at fault, testability analysis algorithms provide an estimated testability value by computing controllability and observability. In most common previous work such as COP and SCOAP, signal correlation between controllability and observability is not well handled. As a result, the estimated values can be quite inaccurate. On the other hand, some previous work can take into account signal correlation but may require more CPU time. This paper discusses an efficient method for testability analysis improvement. Our algorithm starts with results obtained from conventional testability analysis such as COP. For each stuck- at fault, we gradually refine these results by recursively applying some simple signal correlation rules. Experimental results show that, with reasonable run-time overhead, significant improvement for testability analysis can be achieved.
机译:[[摘要]]为了预测测试导线卡住的故障的难度,可测性分析算法通过计算可控性和可观察性来提供估计的可测性值。在诸如COP和SCOAP之类的最常见的先前工作中,可控性和可观察性之间的信号相关性没有得到很好的处理。结果,估计值可能非常不准确。另一方面,某些先前的工作可以考虑信号相关性,但可能需要更多的CPU时间。本文讨论了一种可测试性分析改进的有效方法。我们的算法从常规可测试性分析(例如COP)获得的结果开始。对于每个卡在故障处,我们通过递归应用一些简单的信号相关规则来逐步完善这些结果。实验结果表明,在合理的运行时间开销下,可测试性分析可以显着提高。

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