Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures
The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/18/3810 |
| _version_ | 1827725478490800128 |
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| author | Dahye Lee Inhong Chang Hoang Pham |
| author_facet | Dahye Lee Inhong Chang Hoang Pham |
| author_sort | Dahye Lee |
| collection | DOAJ |
| description | The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset. |
| first_indexed | 2024-03-10T22:29:52Z |
| format | Article |
| id | doaj.art-51617be72df44a628007b2f806562036 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:29:52Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-51617be72df44a628007b2f8065620362023-11-19T11:47:56ZengMDPI AGMathematics2227-73902023-09-011118381010.3390/math11183810Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent FailuresDahye Lee0Inhong Chang1Hoang Pham2Department of Computer Science and Statistics, Chosun University, 146 Chosundae-gil, Dong-gu, Gwangju 61452, Republic of KoreaDepartment of Computer Science and Statistics, Chosun University, 146 Chosundae-gil, Dong-gu, Gwangju 61452, Republic of KoreaDepartment of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USAThe coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.https://www.mdpi.com/2227-7390/11/18/3810software reliability growth modelnonhomogeneous Poisson processuncertain operating environmentdependent failuresequential probability ratio test |
| spellingShingle | Dahye Lee Inhong Chang Hoang Pham Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures Mathematics software reliability growth model nonhomogeneous Poisson process uncertain operating environment dependent failure sequential probability ratio test |
| title | Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures |
| title_full | Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures |
| title_fullStr | Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures |
| title_full_unstemmed | Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures |
| title_short | Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures |
| title_sort | study of a new software reliability growth model under uncertain operating environments and dependent failures |
| topic | software reliability growth model nonhomogeneous Poisson process uncertain operating environment dependent failure sequential probability ratio test |
| url | https://www.mdpi.com/2227-7390/11/18/3810 |
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