Search-based prediction of fault count data

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Wasif Afzal, Richard Torkar, Robert Feldt
Title: Search-based prediction of fault count data
Conference name: 1st Internation Symposium on Search Based Software Engineering
Year: 2009
Pagination: 35-38
ISBN: 978-0-7695-3675-0
Publisher: IEEE Computer Society
City: Windsor
ISI number: 000268319000004
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
Authors e-mail:
Language: English
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.
Subject: Software Engineering\General
Computer Science\General
Keywords: search-based, fault prediciton