• DocumentCode
    1638697
  • Title

    The Art and Science of Analyzing Software Data; Quantitative Methods

  • Author

    Menzies, Tim ; Minku, Leandro ; Peters, Fayola

  • Author_Institution
    Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
  • Volume
    2
  • fYear
    2015
  • Firstpage
    959
  • Lastpage
    960
  • Abstract
    Using the tools of quantitative data science, software engineers that can predict useful information on new projects based on past projects. This tutorial reflects on the state-of-the-art in quantitative reasoning in this important field. This tutorial discusses the following: (a) when local data is scarce, we show how to adapt data from other organizations to local problems; (b) when working with data of dubious quality, we show how to prune spurious information; (c) when data or models seem too complex, we show how to simplify data mining results; (d) when the world changes, and old models need to be updated, we show how to handle those updates; (e) when the effect is too complex for one model, we show to how reason over ensembles.
  • Keywords
    data analysis; data mining; software quality; data mining; quantitative data science tools; quantitative methods; software data analysis; software engineers; Art; Computer science; Data mining; Data models; Software; Software engineering; Tutorials;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering (ICSE), 2015 IEEE/ACM 37th IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICSE.2015.306
  • Filename
    7203128