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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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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