Predicting for MTBF Failure Data Series of Software Reliability by Genetic Programming Algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/isda/ZhangC06,
  title =        "Predicting for {MTBF} Failure Data Series of Software
                 Reliability by Genetic Programming Algorithm",
  author =       "Yongqiang Zhang and Huashan Chen",
  publisher =    "IEEE Computer Society",
  year =         "2006",
  booktitle =    "Sixth International Conference on Intelligent Systems
                 Design and Applications (ISDA'06)",
  pages =        "666--670",
  editor =       "Bo Yang and Yuehui Chen",
  address =      "Jinan University, China",
  month =        "16-18 " # oct,
  bibdate =      "2007-01-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/isda/isda2006-1.html#ZhangC06",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-2528-8",
  DOI =          "doi:10.1109/ISDA.2006.218",
  abstract =     "At present, most of software reliability models have
                 to build on certain presuppositions about software
                 fault process, which also brings on the incongruence of
                 software reliability models application. To solve these
                 problems and cast off traditional models
                 multi-subjective assumptions, this paper adopts Genetic
                 Programming (GP) evolution algorithm to establishing
                 software reliability model based on mean time between
                 failures (MTBF) time series. The evolution model of GP
                 is then analysed and appraised according to five
                 characteristic criteria for some common-used software
                 testing cases. Meanwhile, we also select some
                 traditional probability models and the Neural Network
                 Model to compare with the new GP model separately. The
                 result testifies that the new model evolved by GP has
                 the higher prediction precision and better
                 applicability, which can improve the applicable
                 inconsistency of software reliability modelling to some
                 extent.",
  notes =        "http://isda2006.ujn.edu.cn/ Yongqiang Zhang, Hebei
                 University of Engineering, China Huashan Chen, Hebei
                 University of Engineering, China",
}

Genetic Programming entries for Yongqiang Zhang Huashan Chen

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