Testing is a vital phase in software development, and having the right amount of test data is an important aspect in speeding up the process. As a result of the integrationist optimization challenge, extensive testing may not always be practicable. There is also a shortage of resources, expenses, and schedules that impede the testing process. One way to explain combinational testing (CT) is as a basic strategy for creating new test cases. CT has been discussed by several scholars while establishing alternative tactics depending on the interactions between parameters. Thus, an investigation into current CT methods was started in order to better understand their capabilities and limitations. In this study, 97 publications were evaluated based on a variety of criteria, including the generation technology, test strategy method, supported interactions, mixed coverage ,and support constraints between parameters. CT analysis had a wide range of interaction assistance options available to researchers. Since 2010, a unified interaction has been the most common style of interaction between the two parties. The year 2018 was hailed as the most successful in terms of CT by researchers. Researchers should focus on one test at a time and metaheuristic search strategies for t-way CT. There has also been a significant increase in the popularity of other trends, such as deep learning (DL). CT appears to be a useful testing technique for balancing and fault detection capabilities for a variety of systems and applications, according to our research. Future research and software development may benefit from this information. Index Terms— Combinatorial Testing, Test Case Generation, Optimization Algorithms, Software Testing, Artificial Intelligent.
