Predicting for MTBF Failure Data Series of Software Reliability by Genetic Programming Algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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
Citations