Construction of Mixed Covering Arrays for Pair-wise Testing Using Probabilistic Approach in Genetic Algorithm

TLDR: The algorithm PTSG-GA is an extension of previous work that applies genetic algorithm (GA) to generate optimal test set for pair-wise testing and uses a probabilistic approach to generate initial population of CAs/MCAs to improve the performance of GA.

Abstract: In a system with large number of input parameters, it is necessary to check for errors that can occur as a result of interactions between various input parameters. However, checking of all possible combinations of input parameters is often restricted due to time and budget constraints. In order to overcome the constraints of exhaustive testing, combinatorial testing has been employed to generate optimal and efficient test set that covers all t-way combinations of input parameters. Pair-wise testing, a combinatorial testing technique, tests all possible combinations of each pair of input parameter values. In this paper, we present an efficient algorithm pair-wise test set generator using genetic algorithm (PTSG-GA) for generating test set for pair-wise testing. PTSG-GA is an extension of our previous work that applies genetic algorithm (GA) to generate optimal test set for pair-wise testing. In this paper, combinatorial objects, namely covering array (CA) and mixed covering arrays (MCA), are used to represent test set. The major contribution of algorithm PTSG-GA is that it uses a probabilistic approach to generate initial population of CAs/MCAs to improve the performance of GA. The algorithm PTSG-GA is implemented using an open-source tool PWiseGen. We have reported experimental results that illustrate the effectiveness of PTSG-GA as compared to existing state-of-the-art algorithms.