Zobrazit v EDS

A generalized multi-upgradation SRGM considering uncertainty of random field operating environments.

Uloženo v:
Podrobná bibliografie
Název: A generalized multi-upgradation SRGM considering uncertainty of random field operating environments.
Autoři: Mishra, Gaurav, Kapur, P. K., Aggarwal, Anu G.
Zdroj: International Journal of Systems Assurance Engineering & Management; Mar2023 Suppl 1, Vol. 14, p328-336, 9p
Abstrakt: Nowadays software companies are releasing upgraded versions of applications or software on a weekly, fortnightly, and monthly basis. This is mainly to meet the customer's requirements and beat the market competition in terms of features, speed, reliability, security, and many more attributes like iOS, Android, Facebook, and others. Finally, this shows the importance of the multi-up-gradation of the software. During the last 4 decades, many SRGMs have been presented for single and multi-up-gradation of the applications to measure the number of bugs and reliability of applications. All these SRGMs were presented in the fixed settings of the software development environment, which is very much predictable. But after the release of the software, the field operating environment is very much unpredictable and random. That is the reason we call it a random field operating environment (RFOE). Numerous SRGMs have been presented with the assumption that operating environments and development settings are similar. We are unaware of the operating environments' uncertainties because these two environments are much different in practice. In this paper, we presented two multi-upgradation SRGMs to capture the uncertainty of bug detection rate per unit of time in the RFOE. We have examined the attainment of the given models using an actual failure data set. The results reveal that the goodness of fit and prognostication performance has improved significantly. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Systems Assurance Engineering & Management is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Popis
Abstrakt:Nowadays software companies are releasing upgraded versions of applications or software on a weekly, fortnightly, and monthly basis. This is mainly to meet the customer's requirements and beat the market competition in terms of features, speed, reliability, security, and many more attributes like iOS, Android, Facebook, and others. Finally, this shows the importance of the multi-up-gradation of the software. During the last 4 decades, many SRGMs have been presented for single and multi-up-gradation of the applications to measure the number of bugs and reliability of applications. All these SRGMs were presented in the fixed settings of the software development environment, which is very much predictable. But after the release of the software, the field operating environment is very much unpredictable and random. That is the reason we call it a random field operating environment (RFOE). Numerous SRGMs have been presented with the assumption that operating environments and development settings are similar. We are unaware of the operating environments' uncertainties because these two environments are much different in practice. In this paper, we presented two multi-upgradation SRGMs to capture the uncertainty of bug detection rate per unit of time in the RFOE. We have examined the attainment of the given models using an actual failure data set. The results reveal that the goodness of fit and prognostication performance has improved significantly. [ABSTRACT FROM AUTHOR]
ISSN:09756809
DOI:10.1007/s13198-023-01859-7