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The optimal community detection of software based on complex networks

College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

E-mail Address: hgy@ysu.edu.cn

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Peng Zhang

College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

E-mail Address: zhpenglv@163.com

Corresponding author.

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Bing Zhang

College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

E-mail Address: zhang-bing@hotmail.com

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Tengteng Yin

College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

E-mail Address: raniy@sina.cn

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Jiadong Ren

College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China

E-mail Address: jdren@ysu.edu.cn

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https://doi.org/10.1142/S0129183116500856Cited by:7 (Source: Crossref)

Abstract

The community structure is important for software in terms of understanding the design patterns, controlling the development and the maintenance process. In order to detect the optimal community structure in the software network, a method Optimal Partition Software Network (OPSN) is proposed based on the dependency relationship among the software functions. First, by analyzing the information of multiple execution traces of one software, we construct Software Execution Dependency Network (SEDN). Second, based on the relationship among the function nodes in the network, we define Fault Accumulation (FA) to measure the importance of the function node and sort the nodes with measure results. Third, we select the top K(K=1,2,…) nodes as the core of the primal communities (only exist one core node). By comparing the dependency relationships between each node and the K communities, we put the node into the existing community which has the most close relationship. Finally, we calculate the modularity with different initial K to obtain the optimal division. With experiments, the method OPSN is verified to be efficient to detect the optimal community in various softwares.

Keywords:

PACS: 89.75.Hc, 07.05.Tp, 01.50.hv

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