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The optimal community detection of software based on complex networks

Author

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  • Guoyan Huang

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China)

  • Peng Zhang

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China)

  • Bing Zhang

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China)

  • Tengteng Yin

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China)

  • Jiadong Ren

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China2The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China)

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.

Suggested Citation

  • Guoyan Huang & Peng Zhang & Bing Zhang & Tengteng Yin & Jiadong Ren, 2016. "The optimal community detection of software based on complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(08), pages 1-19, August.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:08:n:s0129183116500856
    DOI: 10.1142/S0129183116500856
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