­Automated Generation of Covering Array Using Gravitational Search Algorithm and Biogeography Based Optimization

Abstract

Abstract The utilization of combinatorial testing methodologies in software development has become widespread, necessitating the development of efficient strategies for creating high-quality test suites. Covering Array (CA) has emerged as a key component of combinatorial testing, offering various types to fulfill diverse testing requirements. Several strategies have been introduced for generating CAs, each with its own strengths and weaknesses in terms of performance and efficiency. However, there is still a gap in the existence of a strategy that effectively addresses both aspects simultaneously. Moreover, manually collecting software information increases the likelihood of errors and presents challenges due to the complexity of extracting relevant data. To tackle these challenges, this study employs the GROOVE model checker to automate the extraction of variables and their interactions within the software. By adapting the Gravitational Search Algorithm (GSA) and Biogeography Based Optimization (BBO), an optimal test suite is generated with enhanced efficiency. The primary objective of this paper is to develop a software model using the GROOVE model checker and utilize its capabilities to extract essential software information. The proposed methodology utilizes GSA and BBO to create CAs with both uniform and variable strength. Additionally, a mechanism is introduced to expedite search operations within data structures. To assess the efficacy of the proposed approach, it is implemented within the GROOVE environment, alongside various other meta-heuristic algorithms. Furthermore, the proposed algorithm is also externally implemented for comparison with existing strategies. The evaluation results indicate that the proposed solution surpasses other strategies in terms of efficiency and performance.