On improving genetic optimization based test generation

Test generation procedures based on genetic optimization were shown to be effective in achieving high fault coverage for benchmark circuits. In a genetic optimization procedure, the crossover operator accepts two test patterns t₁ and t₂, and randomly copies parts of t₁ and parts of t₂ into one or more new test patterns. Such a procedure does not take advantage of circuit properties that may aid in generating more effective test patterns. In this work, we propose a representation of test patterns where subsets of inputs are considered as indivisible entities. Using this representation, crossover 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 captured and carried over to the new test patterns. The effectiveness of this scheme is demonstrated by experimental results