• DocumentCode
    2272261
  • Title

    An empirical study on bug assignment automation using Chinese bug data

  • Author

    Lin, Zhongpeng ; Shu, Fengdi ; Yang, Ye ; Hu, Chenyong ; Wang, Qing

  • Author_Institution
    Inst. of Software, Chinese Acad. of Sci., Beijing, China
  • fYear
    2009
  • fDate
    15-16 Oct. 2009
  • Firstpage
    451
  • Lastpage
    455
  • Abstract
    Bug assignment is an important step in bug life-cycle management. In large projects, this task would consume a substantial amount of human effort. To compare with the previous studies on automatic bug assignment in FOSS (free/open source software) projects, we conduct a case study on a proprietary software project in China. Our study consists of two experiments of automatic bug assignment, using Chinese text and the other non-text information of bug data respectively. Based on text data of the bug repository, the first experiment uses SVM to predict bug assignments and achieve accuracy close to that by human triagers. The second one explores the usefulness of non-text data in making such prediction. The main results from our study includes that text data are most useful data in the bug tracking system to triage bugs, and automation based on text data could effectively reduce the manual effort.
  • Keywords
    program debugging; public domain software; software maintenance; statistical analysis; support vector machines; text analysis; Chinese bug textual data; FOSS; SVM; automatic bug assignment; bug life-cycle management; bug repository; empirical study; free/open source software project; triage bug tracking system; Automation; Computer bugs; Data mining; Engineering management; Humans; Open source software; Software debugging; Software engineering; Software measurement; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Empirical Software Engineering and Measurement, 2009. ESEM 2009. 3rd International Symposium on
  • Conference_Location
    Lake Buena Vista, FL
  • ISSN
    1938-6451
  • Print_ISBN
    978-1-4244-4842-5
  • Electronic_ISBN
    1938-6451
  • Type

    conf

  • DOI
    10.1109/ESEM.2009.5315994
  • Filename
    5315994