• DocumentCode
    3232212
  • Title

    Automated Test Data Generation Algorithm Based on Reversed Binary Tree

  • Author

    Li, Jun-Yi ; Sun, Jia-Guang

  • Author_Institution
    Hunan Univ., Changsha
  • Volume
    3
  • fYear
    2007
  • fDate
    July 30 2007-Aug. 1 2007
  • Firstpage
    1124
  • Lastpage
    1128
  • Abstract
    Automated test data generation technology is a hot researching area in automated software test. Some kinds of new concepts such as base node, control node, definition node and definition related control node set are proposed, and in addition, a solution to obtain the definition related control node set is designed, on the basis of combining both the method of linear approximation and DUC expression, an innovative algorithm based on reversed binary tree for automated test data generation is proposed, which can automatically find out all of the feasible paths in the program from the source node to the base node, and can automatically generate the test data for each founded feasible path also. Thereafter, the automated test data generation problem in the software tests has been solved finally. Algorithm analyses show that the newly proposed algorithm has the good features such as simple coding and better efficiency.
  • Keywords
    function approximation; program testing; tree data structures; DUC expression; automated software testing; automated test data generation algorithm; bifurcation function approximation; definition related control node set; linear approximation; reversed binary tree; Algorithm design and analysis; Artificial intelligence; Automatic generation control; Automatic testing; Bifurcation; Binary trees; Distributed computing; Input variables; Software engineering; Software testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-0-7695-2909-7
  • Type

    conf

  • DOI
    10.1109/SNPD.2007.418
  • Filename
    4288018