Information Systems Frontiers

, Volume 14, Issue 2, pp 301–315 | Cite as

Modeling the growth of complex software function dependency networks

  • James Ma
  • Daniel ZengEmail author
  • Huimin Zhao


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.


Software systems Complex networks Network growth model Cohesion and coupling 



The authors would like to thank the anonymous reviewers for their constructive comments and Cheng Nie for his help in drawing the network diagrams. The second author wishes to acknowledge funding support from the National Natural Science Foundation of China (60875049, 60621001), the Chinese Academy of Sciences (2F07C01, 2F08N03), and the Ministry of Science and Technology (2006AA010106).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Management Information SystemsThe University of ArizonaTucsonUSA
  2. 2.School of BusinessMenlo CollegeAthertonUSA
  3. 3.The Key Lab of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Sheldon B. Lubar School of BusinessUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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