Cone-based genetic optimization procedure for test generation and its application to n-detections in combinational circuits
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.
genetic optimization test generation combinational circuits