Abstract: Aiming at the shortcomings of ant colony algorithm in solving MOTCP problem,such as slow convergence rate and easy to fall into local optimum,a dynamic multi-objective test case prioritization method for online ant colony pheromone updating was proposed.The method introduces a dynamic reduction idea.Firstly,the initial test case set cove-ring the same requirements is firstly reduced according to the coverage of the requirements by each test case.Secondly,according to whether the test case can detect the error and the severity of the detected error during the execution process,a method for judging the failure degree of the test case is designed.After each iteration of the ant colony,a se-cond reduction is made to the test case in which no error is detected,so as to reduce the number of test cases that the ant colony needs to pass in the next iteration,and the sorting time is greatly reduced by two reductions.At the same time,in the process of each iteration of the ant colony,by considering the influence of the test factor importance degree,the failure degree and the actual execution time on the next round of pheromone,an online ant colony is designed to update the ant colony simultaneously under three influence factors.The pheromone method enables ant colonies to find the next test case faster and more accurately.Finally,this method,traditional ant colony sorting method and multi-objective optimization sorting method were respectively applied to multiple open source software programs for experimental comparison.The simulation results show that the prioritization method of the online update pheromone of the proposed dynamic reduction has great advantages in performance indicators such as defect detection capability and effective execution time,and can detect errors with higher severity at an earlier level.

Key words: Actual execution time, Ant colony algorithm, Dynamic reduction, Priority sorting, Test case failure degree, Test case importance