Assessing IRPS as an efficient pairwise test data generation strategy
Mohammed I. Younis, Kamal Z. Zamli, Mohammad F. J. Klaib, Zainal Hisham Che Soh, Syahrul Afzal Che Abdullah, Nor Ashidi Mat Isa · International Journal of Advanced Intelligence Paradigms · 2009
This paper discusses a novel pairwise test data generation strategy, called Intersection Residual Pair Set Strategy (IRPS), based on an efficient data structure implementation. In doing so, this paper also demonstrates the correctness of IRPS as well as compares its effectiveness against the existing strategies including Automatic Efficient Test Generator (AETG) and its variations, In Parameter Order (IPO), Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), All Pairs, G2Way and Jenny. Empirical results demonstrate that IRPS, in most cases, outperforms other strategies as far as the number of generated test data and the execution time are concerned.