A 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 t₁ and t₂ copies all the values of each subset either from t₁ or from t₂. By keeping input subsets undivided, activation and propagation capabilities of t₁ and t₂ 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