Input-Output Based Relation Combinatorial Testing Using Whale Optimization Algorithm for Generating Near Optimum Number of Test Suite

Abstract

Software testing is necessary to verify that the employed system is confident enough to be used in a specific task according to the required demand. Testing every possible interaction parameter to identify and resolve defects or minimize the number of faults might fall under exhaustive testing. Given the impracticality of testing every possible interaction due to time, budget, and resource constraints, combinatorial testing, which is t-way testing, is adopted to cover parameter interactions efficiently. This research focuses on the Input-Output Based Relations (IOR) testing strategy, which optimizes the test suite size by selecting critical parameters and employing “don’t care” values for non-essential inputs. Combinatorial testing offers an alternative to overcome the problem. This study proposes a combinatorial testing method utilizing the Whale Optimization Algorithm (WOA). The study compares the performance of WOA with various existing strategies, such as Greedy, Density, TVG, Union, ParaOrder, ReqOrder, ITTDG, AURA, Java Algorithm (CTJ), TTSGA, and AFA. Experimental results indicate that WOA outperforms or matches the effectiveness of these strategies in generating smaller test suites, particularly for higher interaction strengths. The findings demonstrate WOA’s competitive advantage in optimizing combinatorial test suites.