On Improving Genetic Optimization based Test Generation
Irith Pomeranz · 1997
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 t 1 and t 2 , and randomly copies parts of t 1 and parts of t 2 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 t 1 or from t 2 . By keeping input subsets undivided, activation and propagation capabilities of t 1 and t 2 are captured and carried over to the new test patterns. The effectiveness of this scheme is demonstrated by experimental results. 1. Introduction Test generation procedures based on genetic optimization [1] were shown to be effective...