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Search-based prediction of fault count data

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

1st International Symposium on Search Based Software Engineering (SSBSE'09)

DOI:

10.1109/SSBSE.2009.17


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.

Bibtex

@inproceedings{Afzal3058,
author = {Wasif Afzal and Richard Torkar and Robert Feldt},
title = {Search-based prediction of fault count data},
month = {May},
year = {2009},
booktitle = {1st International Symposium on Search Based Software Engineering (SSBSE'09)},
url = {http://www.es.mdu.se/publications/3058-}
}