SCIPOG: Seeding and constraint support in IPOG strategy for combinatorial t-way testing to generate optimum test cases

Abstract

Combinatorial t-way technique is efficient in generating test data and addressing the problem of combinatorial explosion. When constructing a test case, numerous literatures classified t-way strategies into two basic approaches as either One-test-at-a-time approach (OTAT) and One-parameter-at-a-time approach (OPAT). At least three major challenge groups can be encountered when creating test cases. The first one is provision of parameters seeding support that will improve the software quality. The second involves automatically obtaining data regarding parameter constraints and identifying interactions between systemcomponents. The last one is the execution speed and the test suite size. However, in all the existing OPAT t-way strategies, given that the system is loaded with this information, testing present-day software systems is made difficult or impossible. This study presents an effective combinatorial t-way test case generation strategy named Seeding and Constraint support in In-Parameter-Order Generalized (SCIPOG), to develop an improved paired testing approach. However, the study examines the present state-of-the-art and compares several OPAT strategies found in the literature. Moreover, experiments are discussed as part of this process to demonstrate the correctness of the implementation. When statistically analyzing the findings, two non-parametric tests—the Wilcoxon Rank and Friedman tests–were run. SCIPOG, however, produced competitive results. Finally, SCIPOG showed the efficiency of the two proposed methods, which are seeding and constraint support in IPOG strategy.