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Software reliability growth models based on local polynomial modeling with kernel smoothing

Dharmasena, L. Sandamali, Zeephongsekul, P. and Jayasinghe, Chathuri L. 2011, Software reliability growth models based on local polynomial modeling with kernel smoothing, in ISSRE 2011 : Proceedings of the 22nd IEEE International Symposium on Software Reliability Engineering, Institute of Electrical and Electronics Engineers, [Hiroshima, Japan], pp. 220-229.

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Title Software reliability growth models based on local polynomial modeling with kernel smoothing
Author(s) Dharmasena, L. Sandamali
Zeephongsekul, P.
Jayasinghe, Chathuri L.
Conference name International Symposium on Software Reliability Engineering (22nd : 2011 : Hiroshima, Japan)
Conference location Hiroshima, Japan
Conference dates 29 Nov-2 Dec, 2011
Title of proceedings ISSRE 2011 : Proceedings of the 22nd IEEE International Symposium on Software Reliability Engineering
Editor(s) [Unknown]
Publication date 2011
Start page 220
End page 229
Publisher Institute of Electrical and Electronics Engineers
Place of publication [Hiroshima, Japan]
Keyword(s) software reliability growth models
local polynomial regression
convex combination of estimators
Summary Software reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.
ISBN 9781457720604
ISSN 1071-9458
Language eng
Field of Research 080309 Software Engineering
010405 Statistical Theory
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30041826

Document type: Conference Paper
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