Integrating greedy based approach with genetic algorithm to generate mixed covering arrays for pair-wise testing

The effectiveness of combinatorial interaction testing (CIT) to test highly configurable systems has constantly motivated researchers to look out for new techniques to construct optimal covering arrays that correspond to test sets. Pair-wise testing is a combinatorial testing technique that generates a pair-wise interaction test set to test all possible combinations of each pair of input parameter value. Meta heuristic techniques have being explored by researchers in past to construct optimal covering arrays for t-way testing (where, t denotes the strength of interaction). In this paper we apply genetic algorithm, a meta heuristic search based optimization algorithm to generate optimal mixed covering arrays for pair-wise testing. Here, we present a novel method that uses a greedy based approach to perform mutation and study the impact of the proposed approach on the performance of genetic algorithm. We describe the implementation of the proposed approach by extending an open source tool PWiseGen. Experimental results indicate that the use of greedy approach to perform mutation improves the performance of genetic algorithm by generating mixed covering arrays with higher fitness level in less number of generations as compared to those generated using other techniques.