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Modeling the growth of complex software function dependency networks

Author

Listed:
  • James Ma

    (The University of Arizona
    Menlo College)

  • Daniel Zeng

    (The University of Arizona
    Chinese Academy of Sciences)

  • Huimin Zhao

    (University of Wisconsin-Milwaukee)

Abstract

Software engineering efforts can potentially benefit much from a good understanding of the structures of existing software systems and the processes governing their development. Towards that end, we study software systems by means of the complex network analysis framework. We model a software package as a network, with nodes representing the functions in the package and edges representing the dependencies among the functions. Our empirical analysis of five widely-adopted open-source software packages reveals a set of interesting features of such networks, which cannot be adequately reproduced by existing complex network models. We then set out to develop a new network growth model, explicitly imitating generally-advocated software development principals, such as divide-and-conquer, modularization, high intra-module cohesion, and low inter-module coupling. Results of our analytical derivations and numeric studies show that our model can more closely reproduce the particular features exhibited by real-world software packages, thus hopefully better explaining the phenomena of concern.

Suggested Citation

  • James Ma & Daniel Zeng & Huimin Zhao, 2012. "Modeling the growth of complex software function dependency networks," Information Systems Frontiers, Springer, vol. 14(2), pages 301-315, April.
    • Handle: RePEc:spr:infosf:v:14:y:2012:i:2:d:10.1007_s10796-010-9239-z
    DOI: 10.1007/s10796-010-9239-z
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    References listed on IDEAS

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    1. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    2. Zheng, Xiaolong & Zeng, Daniel & Li, Huiqian & Wang, Feiyue, 2008. "Analyzing open-source software systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6190-6200.
    3. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
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    Cited by:

    1. Behfar, Stefan Kambiz & Turkina, Ekaterina & Cohendet, Patrick & Burger-Helmchen, Thierry, 2016. "Directed networks’ different link formation mechanisms causing degree distribution distinction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 479-491.
    2. Mehmet N. Aydin & N. Ziya Perdahci, 0. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    3. Mehmet N. Aydin & N. Ziya Perdahci, 2019. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 21(2), pages 229-240, April.
    4. Rezvanian, Alireza & Rahmati, Mohammad & Meybodi, Mohammad Reza, 2014. "Sampling from complex networks using distributed learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 224-234.

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