关键词: 测试用例生成, 弱变异测试, 分支覆盖, 集合进化, 贪心算法

Abstract: Abstract: To solve the problem of fixed individual scale and high execution cost of weak mutation test set generation based on Set Evolutionary Algorithm, a new method called Dynamic Set Evolutionary Algorithm was proposed. In this method, different test sets have been made as individuals to generate some weak mutations to cover all mutant branches. In the evolutionary process, a reduction operator on those sets was presented to adjust the scale of individuals. Then a suitable fitness function has been designed to evaluate those test sets. First, the minimal size of test sets has been calculated according to the minimum subset of the optimal individual and the number of the optimal individual uncovered mutant branches. Then the size of all individuals was adjusted based on a test set minimal size. Finally, the minimal weak mutation test set was generated. In experiments, the results show that weak mutation test set generation guided by dynamic Set Evolutionary Algorithm can effectively reduce the scale of the generated test set. The minimized size is about 50.15% on average than the initial size of the individual. And the execution time is also lower than the original algorithm, which has a maximum reduction of 74.58%. Therefore, this method provides a better and faster solution for a minimal weak mutation test set generation.

Key words: test case generation, Weak mutation testing, Branch coverage, Set evolutionary algorithm, Greedy algorithm