• DocumentCode
    3194875
  • Title

    Machine learning and software engineering

  • Author

    Zhang, El ; Tsai, Jeffrey J P

  • Author_Institution
    Dept. of Comput. Sci., California State Univ., Sacramento, CA, USA
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    22
  • Lastpage
    29
  • Abstract
    Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning methods to so are engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks.
  • Keywords
    learning (artificial intelligence); software engineering; software quality; software reliability; application domains; machine learning; software engineering; software maintenance; Application software; Artificial intelligence; Electrical capacitance tomography; Guidelines; Learning systems; Machine learning; Maintenance engineering; Programming; Software engineering; Software maintenance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-1849-4
  • Type

    conf

  • DOI
    10.1109/TAI.2002.1180784
  • Filename
    1180784