To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

Selecting software reliab… - University of Gothenburg, Sweden Till startsida
Sitemap
To content Read more about how we use cookies on gu.se

Selecting software reliability growth models and improving their predictive accuracy using historical projects data

Journal article
Authors Rakesh Rana
Miroslaw Staron
Christian Berger
Jörgen Hansson
M. Nilsson
F. Torner
W. Meding
C. Hoglund
Published in Journal of Systems and Software
Volume 98
Pages 59-78
ISSN 0164-1212
Publication year 2014
Published at Department of Computer Science and Engineering (GU)
Pages 59-78
Language en
Links dx.doi.org/10.1016/j.jss.2014.08.03...
Keywords Embedded software, Defect inflow, Software reliability growth models, ERROR-DETECTION, SYSTEMS, Computer Science, Software Engineering, Computer Science
Subject categories Electrical Engineering, Electronic Engineering, Information Engineering

Abstract

During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain. Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model. (C) 2014 Elsevier Inc. All rights reserved.

Page Manager: Webmaster|Last update: 9/11/2012
Share:

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?