Search-Based Prediction of Fault Count Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Afzal:2009:SSBSE,
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author = "Wasif Afzal and Richard Torkar and Robert Feldt",
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title = "Search-Based Prediction of Fault Count Data",
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booktitle = "Proceedings 1st International Symposium on Search
Based Software Engineering SSBSE 2009",
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year = "2009",
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editor = "Massimiliano {Di Penta} and Simon Poulding",
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pages = "35--38",
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address = "Windsor, UK",
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month = "13-15 " # may,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, SBSE,
search-based prediction, software fault count data,
software reliability growth model, symbolic regression,
regression analysis, software fault tolerance",
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isbn13 = "978-0-7695-3675-0",
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DOI = "doi:10.1109/SSBSE.2009.17",
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abstract = "Symbolic regression, an application domain of genetic
programming (GP), aims to find a function whose output
has some desired property, like matching target values
of a particular data set. While typical regression
involves finding the coefficients of a pre-defined
function, symbolic regression finds a general function,
with coefficients, fitting the given set of data
points. The concepts of symbolic regression using
genetic programming can be used to evolve a model for
fault count predictions. Such a model has the
advantages that the evolution is not dependent on a
particular structure of the model and is also
independent of any assumptions, which are common in
traditional time-domain parametric software reliability
growth models. This research aims at applying
experiments targeting fault predictions using genetic
programming and comparing the results with traditional
approaches to compare efficiency gains.",
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notes = "order number P3675 http://www.ssbse.info/ Also known
as \cite{5033177}",
- }
Genetic Programming entries for
Wasif Afzal
Richard Torkar
Robert Feldt
Citations