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Search-based prediction of fault count data
Responsible organisation
2009 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Windsor: IEEE Computer Society , 2009.
Keyword [en]
search-based, fault prediciton
National Category
Software Engineering Computer Science
Identifiers
URN: urn:nbn:se:bth-8089ISI: 000268319000004Local ID: oai:bth.se:forskinfo248E919B72CE2D82C12575C7002931EBISBN: 978-0-7695-3675-0 (print)OAI: oai:DiVA.org:bth-8089DiVA: diva2:835776
Conference
1st Internation Symposium on Search Based Software Engineering
Available from: 2012-09-18 Created: 2009-05-31 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(78 kB)401 downloads
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Torkar, RichardFeldt, Robert
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf