Optimal software testing case research based on self-learning control algorithm

This paper demonstrates an approach to optimizing software testing cases by rapidly fixing software deficiency with given software parameter uncertainty during a regressive testing process. Taking the software testing process into a time-varied system control problem, a state transform matrix model is presented. Because regressive testing is an iterative process, the two-dimensional variable-factor self-learning strategy is used to optimize the test case. The simulation results show that the learning control strategy is better than either random testing or the Markov testing strategy, and it can significantly reduce regressive test numbers and save test costs.