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Community structure of complex software systems: Analysis and applications

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  • Šubelj, Lovro
  • Bajec, Marko

Abstract

Due to notable discoveries in the fast evolving field of complex networks, recent research in software engineering has also focused on representing software systems with networks. Previous work has observed that these networks follow scale-free degree distributions and reveal small-world phenomena, while we here explore another property commonly found in different complex networks, i.e. community structure. We adopt class dependency networks, where nodes represent software classes and edges represent dependencies among them, and show that these networks reveal a significant community structure, characterized by similar properties as observed in other complex networks. However, although intuitive and anticipated by different phenomena, identified communities do not exactly correspond to software packages. We empirically confirm our observations on several networks constructed from Java and various third party libraries, and propose different applications of community detection to software engineering.

Suggested Citation

  • Šubelj, Lovro & Bajec, Marko, 2011. "Community structure of complex software systems: Analysis and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2968-2975.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:16:p:2968-2975
    DOI: 10.1016/j.physa.2011.03.036
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    References listed on IDEAS

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    Cited by:

    1. Li, Hui & Zhao, Hai & Cai, Wei & Xu, Jiu-Qiang & Ai, Jun, 2013. "A modular attachment mechanism for software network evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2025-2037.
    2. Xiao, Guanping & Zheng, Zheng & Wang, Haoqin, 2017. "Evolution of Linux operating system network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 249-258.
    3. Shen, Yi, 2014. "The similarity of weights on edges and discovering of community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 560-570.
    4. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    5. Carlo Piccardi, 2011. "Finding and Testing Network Communities by Lumped Markov Chains," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-13, November.
    6. Šubelj, Lovro & Bajec, Marko, 2014. "Group detection in complex networks: An algorithm and comparison of the state of the art," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 144-156.

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