CAJ | 학술논문

测试性优化分配是在测试性分配中合理配置各项测试资源，以满足测试性分配要求和资源约束条件。一般的遗传算法存在最优解不易求解、初始参数值不易设置等缺陷，因此，为了更加高效合理地进行测试性优化分配，在传统遗传算法中引入迁移技术来优化遗传迭代过程，并运用AHP法对算法初始参数进行调整，从而提出了基于改进的遗传算法的测试性优化分配方法。算例分析表明该方法可快速有效地求得测试性优化分配问题的最优解。
측시성우화분배시재측시성분배중합리배치각항측시자원，이만족측시성분배요구화자원약속조건。일반적유전산법존재최우해불역구해、초시삼수치불역설치등결함，인차，위료경가고효합리지진행측시성우화분배，재전통유전산법중인입천이기술래우화유전질대과정，병운용AHP법대산법초시삼수진행조정，종이제출료기우개진적유전산법적측시성우화분배방법。산례분석표명해방법가쾌속유효지구득측시성우화분배문제적최우해。
In order to meet the testability allocation requirements and resource constraints,test resources are reasonably configured for testability optimization allocation. The optimal solution is not easy to solve of general genetic algorithm,and the initial parameter value is not easy to set up and other defects. So in order to more efficiently test and optimize the allocation,migration technology is introduced in the traditional genetic algorithm to optimize the iterative process,and initial parameters of algorithm can be adjusted by using AHP method,consequently testability optimization allocation method based on improved genetic algorithm is proposed. Examples show that this method can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.