Abstract
Cloud migration is an important means of software development on the cloud. The identification of reusable components of legacy systems directly determines the quality of cloud migration. The existed clustering algorithms do not consider the factor of relation types between classes, which affects the accuracy of clustering result. In this paper, the relation type information between classes is introduced in software clustering. Multi-objective genetic algorithm is used to cluster the module dependency graph with the relationship types (R-MDG). The experimental results show that the above method can effectively improve the quality of reusable components.
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Acknowledgment
The authors wish to thank Natural Science Foundation of China under Grant No. 61462066, 61662054, Natural Science Foundation of Inner Mongolia under Grand No. 2015MS0608, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating”. Inner Mongolia Engineering Lab of Cloud Computing and Service Software and Inner Mongolia Engineering Lab of Big Data Analysis Technology.
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Zhou, Jt., Wu, T., Chen, Y., Zhao, Jf. (2018). A Method of Component Discovery in Cloud Migration. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_9
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