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Prediction of fault count data using genetic programming
Responsible organisation
2008 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.

Place, publisher, year, edition, pages
Karachi, Pakistan: IEEE , 2008.
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-8313ISI: 000266371600066Local ID: oai:bth.se:forskinfo6A155E169A78DF3DC125751900468EE0ISBN: 978-1-4244-2823-6 (print)OAI: oai:DiVA.org:bth-8313DiVA: diva2:836020
Conference
12th IEEE International Multitopic Conference
Available from: 2012-09-18 Created: 2008-12-08 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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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