Software testing is an integral part of software engineering. Lack of testing often leads to disastrous consequences including loss of data, fortunes, and even lives. In order to ensure software reliability, many combinations of possible input parameters, hardware/software environments, and system configurations need to be tested and verified against for conformance. Due to costing factors as well as time to market constraints, considering all exhaustive test possibilities would be infeasible (i.e. due to combinatorial explosion problem). Earlier work suggests that pairwise sampling strategy (i.e. based on two-way parameter interaction) can be effective. Building and complementing earlier work, this paper discusses an efficient pairwise test data generation strategy, called IRPS. In doing so, IRPS is compared against existing strategies including AETG and its variations, IPO, SA, GA, ACA, and All Pairs. Empirical results demonstrate that IRPS strategy, in most cases, outperformed other strategies as far as the number of test data generated within reasonable time.
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