Search-Based Prediction of Fault Count Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Afzal:2009:SSBSE,

• author = "Wasif Afzal and Richard Torkar and Robert Feldt",
• title = "Search-Based Prediction of Fault Count Data",
• booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009",
• year = "2009",
• editor = "Massimiliano {Di Penta} and Simon Poulding",
• pages = "35--38",
• address = "Windsor, UK",
• month = "13-15 " # may,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, SBSE, search-based prediction, software fault count data, software reliability growth model, symbolic regression, regression analysis, software fault tolerance",
• isbn13 = "978-0-7695-3675-0",
• DOI = "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.",
• notes = "order number P3675 http://www.ssbse.info/ Also known as \cite{5033177}",

}

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt

Citations