Detecting Arrays for Main Effects
Detecting Arrays for Main Effects
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DOI：
10.1007/978-3-030-21363-3_10
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
C. Colbourn;V. Syrotiuk
中科院分区：
文献类型：
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作者：
C. Colbourn;V. Syrotiuk
确定复杂工程系统的正确性和性能需要对系统进行测试，以确定许多因素及其相互作用如何影响其行为。特别值得关注的是确定哪些因素（主效应）设置会显着影响行为。主效应检测阵列是一种测试套件，可确保即使存在或多或少的其他显着主效应，也能见证每个主效应的影响。检测阵列中的分离规定至少存在指定数量的此类目击者。一个新的参数，即佐证，可以在保持证人存在的同时实现级别的融合。使用纠错码和分离散列族构建具有用于分离和佐证的各种值的主效应检测阵列。事实证明，这些技术只需对大量因素进行少量测试即可产生明确的结构。
Determining correctness and performance for complex engineered systems necessitates testing the system to determine how its behaviour is impacted by many factors and interactions among them. Of particular concern is to determine which settings of the factors (main effects) impact the behaviour significantly. Detecting arrays for main effects are test suites that ensure that the impact of each main effect is witnessed even in the presence ofdor fewer other significant main effects. Separation in detecting arrays dictates the presence of at least a specified number of such witnesses. A new parameter, corroboration, enables the fusion of levels while maintaining the presence of witnesses. Detecting arrays for main effects, having various values for the separation and corroboration, are constructed using error-correcting codes and separating hash families. The techniques are shown to yield explicit constructions with few tests for large numbers of factors.