Provide a Solution Based on Teacher and Student Learning Algorithm to Reduce Regression Test Cases

The aim of selecting test items is to choose a subset that has the potential to detect errors due to changes within the software. In other words, the purposes of test selection methods is to reduce the number of test cases after changing the code and focus on identifying the modified parts of the program. Intelligent methods such as regression improve the accuracy of tests in software projects, and the use of optimization algorithms to find the optimal amount of test cases can be useful in terms of time and speed, and according to research by examining and optimizing this algorithm in the system. In this paper, a technique for reducing regression test cases based on teacher-student optimization method was presented. This method was studied in two stages of teacher (education phase) and student (learning phase) on the test set and was implemented with different parameters. The experimental results showed that the use of the teacher-student algorithm reduces the time required for the reduction parameters of the regression test to some extent, although it does not give us a definite answer and will give a near-optimal answer. Also, the results of teacher-student algorithm were compared with previous approaches of regression test case reduction. Experimental results show better average execution time for test case selection. .