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A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data
Ansvarlig organisasjon
2008 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert) Published
Abstract [en]

There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.

sted, utgiver, år, opplag, sider
IEEE , 2008.
Emneord [en]
Genetic programming, fault count predictions
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-8310DOI: 10.1109/ICSEA.2008.9Lokal ID: oai:bth.se:forskinfoF19969F37EAB7F70C125751900486136ISBN: 978-1-4244-3218-9 (tryckt)OAI: oai:DiVA.org:bth-8310DiVA, id: diva2:836017
Konferanse
Software Engineering Advances, 2008. ICSEA '08. The Third International Conference on
Tilgjengelig fra: 2012-09-18 Laget: 2008-12-08 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Totalt: 710 treff
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  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
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  • asciidoc
  • rtf