Increasing Error Detection in Software Testing Using Cuckoo Algorithm and Gravity Search Algorithm

In the realm of software testing, one of the most critical challenges faced by development teams is the limitation of resources and time. As software systems grow in complexity and size, the number of test cases increases significantly, making it impractical to re-execute the entire suite of tests in each testing cycle. Consequently, there arises a need for effective strategies to select and prioritize test cases in a way that ensures the most valuable and error-prone parts of the software are tested early. Prioritizing test cases not only accelerates the detection of defects but also enhances the efficiency of the testing process by focusing efforts on areas with higher potential for failure. In this study, two powerful nature-inspired metaheuristic algorithms Cuckoo Search Optimization Algorithm and Gravitational Search Algorithm are employed to address the problem of test case prioritization. These algorithms are used to prioritize test cases based on coverage criteria, particularly aiming for maximum fault detection coverage. By optimizing the sequence of test execution, the proposed method improves both the fault detection rate and the overall effectiveness of the testing process. This approach contributes to the timely identification of critical defects and supports faster and more reliable software releases.

کلیدواژه ها:

Software Tsting ، Cuckoo Search Optimization Algorithm ، Gravity Search Algorithm ، Prioritizing Test Items