Discovering Common Features in Software Code Using Self-Organizing Maps

  • Alvin Chan
  • Tim Spracklen
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 5)


The self-organizing map is discussed as an unsupervised clustering method. Its ability to form clusters indicates similar features in a data set. Based on this property, it is demonstrated that a self-organizing map is capable of identifying features within software code by grouping procedures with similar properties together. This allows us to identify potential objects, abstract data types or classes. In experiments with a simulation package Pascal_SIM (a procedural oriented implementation) as the data set, features were identified and a feature matrix constructed that served as the input to the self-organizing map. The results obtained were clusters on the map that indicated procedures with similar features being grouped together. This demonstrates that the self-organizing map is potentially a viable tool in intelligently automating the discovery of common features and groupings within code.


Feature Matrix Software Code Legacy Code Object Instance Potential Object 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Alvin Chan
    • 1
  • Tim Spracklen
    • 1
  1. 1.Electronics Research Group, Engineering DepartmentUniversity of AberdeenScotland

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