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Using Citation Influence and Social Network Analysis to Predict Software Defects

  • Author / Creator
    Hu, Wei
  • Resource constraints, e.g. lack of time and human resources, is a major issue in software testing practice. In short, testers have limited time to test software systems. Therefore, managers are expected to spend more resources on software components that are likely to contain many defects. To help managers make better decisions of selective testing, it is beneficial to identify defect-prone software components before the actual testing. In this thesis, we propose a model for software defect prediction. The proposed model combines the topological properties of the software dependency network and the textual information in source code to predict defect-prone software components. We evaluate our model on data from Eclipse, Netbeans, and Gnome projects at different levels of granularity. The evaluation results are encouraging, showing that our model achieves higher prediction accuracy than prior work.

  • Subjects / Keywords
  • Graduation date
    2013-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3497V
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Wong, Kenny (Computing Science)
  • Examining committee members and their departments
    • Greiner, Russell (Computing Science)
    • Stroulia, Eleni (Computing Science)
    • Barbosa, Denilson (Computing Science)
    • Wong, Kenny (Computing Science)