• Journal of KIISE
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• v.44 no.3
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• pp.275-286
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• 2017

In this study, we propose a novel technique of software clustering to recover the software module-view by using the dependency and author entropy of modules. The proposed method first performs clustering of modules based on structural and logical dependencies, then it migrates selected modules from the clustered result by utilizing the author entropy of each module. In order to evaluate the proposed method, we calculated the MoJoFM values of the recovery result by applying the method to open-source projects among which ground-truth decompositions are well-known. Compared to the MoJoFM values of previously studied techniques, we demonstrated the effectiveness of the proposed method.

• Journal of Software Engineering Society
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• v.25 no.4
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• pp.69-74
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• 2012

This article suggests applicability to community detection algorithm from module recovering process of software architecture through compare to software clustering metric and community dectection metric. in addition to, analyze mutual relation and difference between separated module and measurement value of typical clustering algorithms and community detection algorithms. and then only sugeested several kinds basis that community detection algorithm can use to recovering module view of software architecture and, by so comparing measurement value of existing clustering metric and community algorithms, this article suggested correlation of two result data.