• DocumentCode
    479924
  • Title

    Artificial Neural Network for Automatic Test Oracles Generation

  • Author

    Jin, Hu ; Wang, Yi ; Chen, Nian-Wei ; Gou, Zhi-Jian ; Wang, Shuo

  • Author_Institution
    Comput. Sci. Dept., Chengdu Univ. of Inf. Technol., Chengdu
  • Volume
    2
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    727
  • Lastpage
    730
  • Abstract
    Test Oracle is one of the most important problems to be tackled for automatic software testing. Unlike manual test, by reviewing results to determine whether or not result is what it should expected to be, automatic methods only depend on specified decision rules. Artificial Intelligence is a prominent solution for such work. In this research, Artificial Neural Network was proposed to direct oracles automatic generation. The mainly work includes: Heuristic test oracles fit for automatic software testing were introduced firstly. Next, the classifying and predication capabilities of the Artificial Neural Network were analyzed, which is very suitable for some kinds of test oracles generation. Then, a test oracles generator was designed by using ANN model. Lastly, a specified example was given for validation and evaluation. Experiments showed ANN is proper for automatic test oracles generation for some kinds of programs.
  • Keywords
    formal specification; neural nets; program testing; ANN model; artificial intelligence; artificial neural network; automatic software testing; automatic test oracle generation; formal specification; heuristic test oracle; specified decision rule; Algorithms; Artificial neural networks; Automatic testing; Computer science; Costs; Formal specifications; Information technology; Software engineering; Software testing; System testing; Artificial Neural Network; Automatic Software Testing; Heuristic study; Software under Test; Test Oracle;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.774
  • Filename
    4722154