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A Cone-Based Genetic Optimization Procedure for Test Generation and Its Application to n$n$-Detections in Combinational Circuits
October 1999 (vol. 48 no. 10)
pp. 1145-1152

Abstract—Test generation procedures based on genetic optimization were shown to be effective in achieving high fault coverage for benchmark circuits. In this work, we propose a representation of test patterns for genetic optimization based test generation, where subsets of inputs are considered as indivisible entities. Using this representation, crossover between two test patterns $t_1$ and $t_2$ copies all the values of each subset either from $t_1$ or from $t_2$. By keeping input subsets undivided, activation and propagation capabilities of $t_1$ and $t_2$ are expected to be captured and carried over to the new test patterns. Experimental results presented show that the proposed scheme results in complete stuck-at test sets and $n$-detection test sets for combinational circuits, even in cases where other procedures report incomplete fault coverages.

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Index Terms:
Combinational circuits, genetic optimization, test generation.
Citation:
Irith Pomeranz, Sudhakar M. Reddy, "A Cone-Based Genetic Optimization Procedure for Test Generation and Its Application to n$n$-Detections in Combinational Circuits," IEEE Transactions on Computers, vol. 48, no. 10, pp. 1145-1152, Oct. 1999, doi:10.1109/12.805164
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