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Comprehensive evaluation of software system reliability based on component-based generalized G-O models
The component-based software system has a core that is based on architecture design. Predicting the reliability growth trends of a software system in the early stages of the development process is conducive to reducing waste and loss caused by blind development. Restricted by the lack of information...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280468/ https://www.ncbi.nlm.nih.gov/pubmed/37346610 http://dx.doi.org/10.7717/peerj-cs.1247 |
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author | Wang, Yuzhuo Liu, Haitao Yuan, Haojie Zhang, Zhihua |
author_facet | Wang, Yuzhuo Liu, Haitao Yuan, Haojie Zhang, Zhihua |
author_sort | Wang, Yuzhuo |
collection | PubMed |
description | The component-based software system has a core that is based on architecture design. Predicting the reliability growth trends of a software system in the early stages of the development process is conducive to reducing waste and loss caused by blind development. Restricted by the lack of information and data in the design and integration phase, it is difficult to implement reliability prediction research at this stage. In this article, we focus on a software system in which the reliability of each component follows the G-O model. First, two system-level parameters, which are the total number of system faults and the detection rate of the system faults, are defined. Then, by studying the relationship between the total number of faults and the detection rate of faults between the components and the system, the defined system parameters are calculated from the known component parameters. On this basis, and by incorporating the system parameters, we construct a reliability growth model for the software system, called the component-based generalized G-O model (CB-GGOM). Besides, two approximate models of CB-GGOM are proposed to facilitate reliability evaluation of the software system in the early and stable stages of the integration test. An engineering explanation of the proposed models is also provided, and their effectiveness is verified through simulation and with an authentic example. Since the proposed models are formulated without any integration test data, they are beneficial for developers to optimize test strategies of the software system and implement defect prevention in advance. |
format | Online Article Text |
id | pubmed-10280468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804682023-06-21 Comprehensive evaluation of software system reliability based on component-based generalized G-O models Wang, Yuzhuo Liu, Haitao Yuan, Haojie Zhang, Zhihua PeerJ Comput Sci Scientific Computing and Simulation The component-based software system has a core that is based on architecture design. Predicting the reliability growth trends of a software system in the early stages of the development process is conducive to reducing waste and loss caused by blind development. Restricted by the lack of information and data in the design and integration phase, it is difficult to implement reliability prediction research at this stage. In this article, we focus on a software system in which the reliability of each component follows the G-O model. First, two system-level parameters, which are the total number of system faults and the detection rate of the system faults, are defined. Then, by studying the relationship between the total number of faults and the detection rate of faults between the components and the system, the defined system parameters are calculated from the known component parameters. On this basis, and by incorporating the system parameters, we construct a reliability growth model for the software system, called the component-based generalized G-O model (CB-GGOM). Besides, two approximate models of CB-GGOM are proposed to facilitate reliability evaluation of the software system in the early and stable stages of the integration test. An engineering explanation of the proposed models is also provided, and their effectiveness is verified through simulation and with an authentic example. Since the proposed models are formulated without any integration test data, they are beneficial for developers to optimize test strategies of the software system and implement defect prevention in advance. PeerJ Inc. 2023-02-09 /pmc/articles/PMC10280468/ /pubmed/37346610 http://dx.doi.org/10.7717/peerj-cs.1247 Text en © 2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Scientific Computing and Simulation Wang, Yuzhuo Liu, Haitao Yuan, Haojie Zhang, Zhihua Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title | Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title_full | Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title_fullStr | Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title_full_unstemmed | Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title_short | Comprehensive evaluation of software system reliability based on component-based generalized G-O models |
title_sort | comprehensive evaluation of software system reliability based on component-based generalized g-o models |
topic | Scientific Computing and Simulation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280468/ https://www.ncbi.nlm.nih.gov/pubmed/37346610 http://dx.doi.org/10.7717/peerj-cs.1247 |
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