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Selecting software reliability growth models and improving their predictive accuracy using historical projects data
Computer Science & Engineering, Chalmers/University of Gothenburg, Göteborg, Sweden.
Computer Science & Engineering, Chalmers/University of Gothenburg, Göteborg, Sweden.
Computer Science & Engineering, Chalmers/University of Gothenburg, Göteborg, Sweden.
Computer Science & Engineering, Chalmers/University of Gothenburg, Göteborg, Sweden.ORCID iD: 0000-0003-2895-0780
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2014 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 98, 59-78 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 98, 59-78 p.
National Category
Software Engineering Computer Science
Identifiers
URN: urn:nbn:se:his:diva-14128DOI: 10.1016/j.jss.2014.08.033ISI: 000344421900005Scopus ID: 2-s2.0-84908286057OAI: oai:DiVA.org:his-14128DiVA: diva2:1142594
Available from: 2017-09-19 Created: 2017-09-19 Last updated: 2017-09-25Bibliographically approved

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Hansson, Jörgen
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
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