Prediction of software reliability

Journal article (2021)

Authors

W.D. van Driel Signify, Electronic Components, Technology and Materials - EEMCS
J.W. Bikker Consultants in Quantitative Methods (CQM)
M. Tijink Consultants in Quantitative Methods (CQM)
Research Group
Electronic Components, Technology and Materials (EEMCS) (TU Delft)
Copyright: © 2021 W.D. van Driel, J.W. Bikker, M. Tijink
DOI: https://doi.org/10.1016/j.microrel.2021.114074
Software Bayesian statistics Reliability Maturity growth
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Published Date

2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Electronic Components, Technology and Materials

Abstract

It is known that quantitative measures for the reliability of software systems can be derived from software reliability models. And, as such, support the product development process. Over the past four decades, research activities in this area have been performed. As a result, many software reliability models have been proposed. It was shown that, once these models reach a certain level of convergence, it can enable the developer to release the software. And stop software testing accordingly. Criteria to determine the optimal testing time include the number of remaining errors, failure rate, reliability requirements, or total system cost. In this paper we will present our results in predicting the reliability of software for agile testing environments. We seek to model this way of working by extending the Jelinski-Moranda model to a ‘stack’ of feature-specific models, assuming that the bugs are labelled with the feature they belong to. In order to demonstrate the extended model, several prediction results of actual cases will be presented. The questions to be answered in these cases are: how many software bugs remain in the software and should one decide to stop testing the software?