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

Rakesh Rana ; Miroslaw Staron ; Christian Berger ; Jörgen Hansson (Institutionen för data- och informationsteknik, Software Engineering (Chalmers)) ; M. Nilsson ; F. Torner ; W. Meding ; C. Hoglund
Journal of Systems and Software (0164-1212). Vol. 98 (2014), p. 59-78.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Embedded software, Defect inflow, Software reliability growth models, ERROR-DETECTION, SYSTEMS, Computer Science, Software Engineering, Computer Science



Denna post skapades 2014-12-04. Senast ändrad 2016-09-07.
CPL Pubid: 207346

 

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Institutioner (Chalmers)

Institutionen för data- och informationsteknik (GU) (GU)
Institutionen för data- och informationsteknik, Software Engineering (Chalmers)

Ämnesområden

Elektroteknik och elektronik

Chalmers infrastruktur