Prediction of fault count data using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Afzal08b,
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author = "Wasif Afzal and Richard Torkar and Robert Feldt",
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title = "Prediction of fault count data using genetic
programming",
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booktitle = "Proceedings of the 12th IEEE International Multitopic
Conference (INMIC'08)",
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year = "2008",
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pages = "349--356",
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address = "Karachi, Pakistan",
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month = "23-24 " # dec,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, SBSE, fault
count data, prediction",
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isbn13 = "978-1-4244-2823-6",
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URL = "http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf",
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DOI = "doi:10.1109/INMIC.2008.4777762",
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abstract = "Software reliability growth modeling helps in deciding
project release time and managing project resources. A
large number of such models have been presented in the
past. Due to the existence of many models, the models'
inherent complexity, and their accompanying
assumptions; the selection of suitable models becomes a
challenging task. This paper presents empirical results
of using genetic programming (GP) for modeling software
reliability growth based on weekly fault count data of
three different industrial projects. The goodness of
fit (adaptability) and predictive accuracy of the
evolved model is measured using five different measures
in an attempt to present a fair evaluation. The results
show that the GP evolved model has statistically
significant goodness of fit and predictive accuracy.",
-
notes = "Also known as \cite{4777762}
",
- }
Genetic Programming entries for
Wasif Afzal
Richard Torkar
Robert Feldt
Citations