The process of modifying software can sometimes cause certain functionalities to be affected, leading to an increase in the number of test cases required to ensure that the modified software functions properly. This increase in test cases can result in a significant rise in cost and execution time, making it necessary to minimize the size of the test suite. This article utilizes optimization techniques to enhance the effectiveness of a test suite by identifying the smallest possible set of test cases required to cover all statements, while also minimizing the amount of time needed to run the tests. This research proposes a method to minimize test suite size using a combination of two optimization algorithms: the Artificial Bee Colony (ABC) and Teaching-Learning-Based Optimization (TLBO). The TLBO technique also utilizes a chaotic operator during population exploration. These two techniques are complementary to each other, as the former is capable of exploration, while the latter for exploitation respectively. Their amalgamation gives better result as compared to ABC, TLBO, CABC and CTLBO. Here, CABC and CTLBO are techniques where c denotes the chaotic operator. Although previous studies have combined ABC and TLBO with chaotic operators, this study introduces a new approach that merges ABC and CTLBO. The proposed technique is applied to three benchmark programs, and statistical analysis is performed using a t-test and standard deviation. The experiments showed that the proposed technique outperforms the other techniques in terms of execution cost, average percentage of reduced size, and convergence iteration, indicating that the proposed technique is more scalable

ABC, TLBO, Chaotic map, Test suite minimization; Regression testing