用改进的遗传算法实现架构恢复

高层架构恢复对软件维护和软件进化至关重要.把实现架构恢复的聚类问题看作优化问题,通过对常规遗传算法中初始群体产生策略、选择操作方法、交叉概率和变异概率的自适应性等重要参数和关键环节的改进,设计并实现了混合遗传聚类算法(hybrid genetic clustering algorithm,简称HGCA).同时也对该算法的有效性和综合性能进行了实验分析,结果表明,该算法对初始群体的产生有较好的约束作用.与传统遗传算法相比,它的群体性能和收敛性能都较优,且收敛精度高.同时,基于MoJo度量模型的相似性度量值充分说明了HGCA算法对架构恢复的正确性和有效性.

Implementing Architecture Recovery by Using Improved Genetic Algorithm

Architecture recovery is crucial to supporting software maintenance and evolution. The clustering problem that could implement architecture recovery is considered as optimizing problem in this paper. Through improving important parameters and core steps of general genetic algorithm, such as initial population, select operator, self-adapting ability of crossover probability and mutation probability, a hybrid genetic clustering algorithm (HGCA) is designed and implemented. An experiment is given to analyze the availability, effectiveness and synthetical performance of the algorithm. The results show that compared to general GA, the HGCA can produce good initial population, better convergence efficiency and convergence precision. Moreover, the value of the MoJo similarity metrics presents the correctness and effectiveness of HGCA recovering software architecture.