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  • Software Engineering and Scale-Free Networks

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    52497_1.pdf (784.4Kb)
    Author(s)
    Wen, Lian
    Dromey, R Geoff
    Kirk, Diana
    Griffith University Author(s)
    Dromey, Geoff
    Wen, Larry
    Kirk, Diana
    Year published
    2009
    Metadata
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    Abstract
    Complex-network theory is a new approach in studying different types of large systems in both the physical and the abstract worlds. In this paper, we have studied two kinds of network from software engineering: the component dependence network and the sorting comparison network (SCN). It is found that they both show the same scale-free property under certain conditions as complex networks in other fields. These results suggest that complex-network theory can be a useful approach to the study of software systems. The special properties of SCNs provide a more repeatable and deterministic way to study the evolution and optimization ...
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    Complex-network theory is a new approach in studying different types of large systems in both the physical and the abstract worlds. In this paper, we have studied two kinds of network from software engineering: the component dependence network and the sorting comparison network (SCN). It is found that they both show the same scale-free property under certain conditions as complex networks in other fields. These results suggest that complex-network theory can be a useful approach to the study of software systems. The special properties of SCNs provide a more repeatable and deterministic way to study the evolution and optimization of complex networks. They also suggest that the closer a sorting algorithm is to the theoretical optimal limit, the more its SCN is like a scale-free network. This may also indicate that, to store and retrieve information efficiently, a concept network might need to be scale-free.
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    Journal Title
    I E E E Transactions on Systems, Man and Cybernetics, Part B: Cybernetics
    Volume
    39
    Issue
    3
    Publisher URI
    http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=3477
    DOI
    https://doi.org/10.1109/TSMCB.2008.2008102
    Copyright Statement
    © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Applied Mathematics
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/25961
    Collection
    • Journal articles

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