• DocumentCode
    630444
  • Title

    Machine Learning-Based Software Quality Prediction Models: State of the Art

  • Author

    Al-Jamimi, Hamdi A. ; Ahmed, Mariwan

  • Author_Institution
    Inf. & Comput. Sci. Dept., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
  • fYear
    2013
  • fDate
    24-26 June 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.
  • Keywords
    learning (artificial intelligence); software quality; machine learning; quality molding; software engineering; software quality prediction model; Biological system modeling; Fuzzy logic; Object oriented modeling; Predictive models; Software engineering; Software quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Applications (ICISA), 2013 International Conference on
  • Conference_Location
    Suwon
  • Print_ISBN
    978-1-4799-0602-4
  • Type

    conf

  • DOI
    10.1109/ICISA.2013.6579473
  • Filename
    6579473