Context: To improve the quality and correctness of a software product it is necessary to test different aspects of the software system. Among different approaches for software testing, combinatorial testing along with covering array is a proper testing method. The most challenging problem in combinatorial testing strategies like t-way, is the combinatorial explosion which considers all combinations of input parameters. Many evolutionary and metaheuristic strategies have been proposed to address and mitigate this problem. Objective: Genetic Algorithm (GA) is an evolutionary search-based technique that has been used in t-way interaction testing by different approaches. Although useful, all of these approaches can produce test suite with small interaction strengths (i.e. t ≤6). Additionally, most of them suffer from expensive computations. Even though there are other strategies which use different meta-heuristic algorithms to solve these problems, in this paper, we propose an efficient uniform and variable t-way minimal test suite generation approach to address these problems using GA, called Genetic Strategy (GS). Method: By changing the bit structure and accessing test cases quickly, GS improves performance of the fitness function. These adjustments and reduction of the complexities of GA in the proposed GS decreases the test suite size and increases the speed of test suite generation up to =t 20 . Results: To evaluate the efficiency and performance of the proposed GS, various experiments are performed on different set of benchmarks. Experimental results show that not only GS supports higher interaction strengths in comparison with the existing GA-based strategies, but also its supported interaction strength is higher than most of other AI-based and computational-based strategies. Conclusion: Furthermore, experimental results show that GS can compete against the existing (both AI-based and computational-based) strategies in terms of efficiency and performance in most of the case
