• Login
    View Item 
    •   SMARTech Home
    • Undergraduate Research Opportunities Program (UROP)
    • Undergraduate Research Option Theses
    • View Item
    •   SMARTech Home
    • Undergraduate Research Opportunities Program (UROP)
    • Undergraduate Research Option Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Dynamic Approach to Statistical Debugging: Building Program Specific Models with Neural Networks

    Thumbnail
    View/Open
    thesis.pdf (216.8Kb)
    Date
    2007-05
    Author
    Wood, Matthew
    Metadata
    Show full item record
    Abstract
    Computer software is constantly increasing in complexity; this requires more developer time, effort, and knowledge in order to correct bugs inevitably occurring in software production. Eventually, increases in complexity and size will make manually correcting programmatic errors impractical. Thus, there is a need for automated software-debugging tools that can reduce the time and effort required by the developer. The performance of previously developed debugging techniques can be greatly improved by combining them with machine-learning. Our research focuses on the application of neural networks within the domain of statistical debugging. Specifically, we develop methods to mine statistical debugging data that can then be used to train neural networks; these generated multi-layered neural networks can then be used to identify suspicious programmatic entities. Our developed networks are generated on a per program basis in order to leverage specific programmatic properties. In our empirical evaluation we compare our proposed approach with a state-of-the-art automated debugging technique. The results of the evaluation indicate that, for the cases considered, our approach is more effective than the considered technique.
    URI
    http://hdl.handle.net/1853/16121
    Collections
    • School of Computer Science Undergraduate Research Option Theses [205]
    • Undergraduate Research Option Theses [862]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology