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Search-based prediction of fault count data
Blekinge Institute of Technology. (IS (Embedded Systems))ORCID iD: 0000-0003-0611-2655
Blekinge Institute of Technology.
Blekinge Institute of Technology.
2009 (English)In: Proceedings - 1st International Symposium on Search Based Software Engineering, SSBSE 2009, 2009, p. 35-38Conference paper, Published paper (Refereed)
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 conceptsof symbolic regression using genetic programming can be used to evolve a model for fault countpredictions.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 predictionsusing genetic programming and comparing the results with traditional approaches to compare efficiency gains

Place, publisher, year, edition, pages
2009. p. 35-38
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-22274DOI: 10.1109/SSBSE.2009.17Scopus ID: 2-s2.0-70349284908ISBN: 9780769536750 (print)OAI: oai:DiVA.org:mdh-22274DiVA, id: diva2:661361
Conference
1st International Symposium on Search Based Software Engineering, SSBSE 2009; Windsor, Berkshire; United Kingdom; 13 May 2009 through 15 May 2009
Projects
Project_ExternalAvailable from: 2013-11-03 Created: 2013-10-31 Last updated: 2013-12-03Bibliographically approved

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Afzal, Wasif

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CiteExportLink to record
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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