• DocumentCode
    2709167
  • Title

    Software reliability model by AGP

  • Author

    Zhang, Yongqiang ; Yin, Jingjie

  • Author_Institution
    Hebei Univ. of Eng., Handan
  • fYear
    2008
  • fDate
    21-24 April 2008
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    To solve the problems of the incongruence of software reliability models and cast off the traditional models´ multi-subjective assumptions, this paper adopts genetic programming evolution algorithm which has adaptive genetic operators (for short AGP) to establish software reliability model based on software failure time series. The individual of the population is according to the case of the fitness of the generation to adjust the probability of crossover and mutation by the sigmoid curve. By evaluating the data series of the software testing case in Armored Force Engineering Institute, the results sufficiently testify that the new AGP algorithm has better applicability and the validity of fitness and forecasting. Moreover, compared with standard genetic programming evolution algorithm, the new AGP algorithm has the better rapidity of convergence. Therefore, we can say that, this algorithm can be more effectively applied to software testing and ensured the validity of data.
  • Keywords
    genetic algorithms; program testing; software reliability; AGP algorithm; Armored Force Engineering Institute; adaptive genetic operators; genetic programming evolution algorithm; sigmoid curve; software failure time series; software reliability model; software testing; Convergence; Data engineering; Genetic mutations; Genetic programming; Life estimation; Phase estimation; Reliability engineering; Software algorithms; Software reliability; Software testing; GP; adaptive genetic operators; reliability model; software reliability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Technology, 2008. ICIT 2008. IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-1705-6
  • Electronic_ISBN
    978-1-4244-1706-3
  • Type

    conf

  • DOI
    10.1109/ICIT.2008.4608638
  • Filename
    4608638