| Autor: |
Timothy Mutunga Ndonye, Albert Orwa Akuno, Janiffer Mwende Nthiwa, Luke Akong’o Orawo |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
American Journal of Theoretical and Applied Statistics. 5:80 |
| ISSN: |
2326-8999 |
| DOI: |
10.11648/j.ajtas.20160503.11 |
| Popis: |
Mathematical studies about the likelihood of failures of software systems have been advanced by various researchers. These studies have modeled the behavior of software systems by using failure times and time between failures in the past. The Goel-Okumoto software reliability model is amongst the many software reliability models proposed to model the failure behavior of software systems. To be able to use the model in software reliability assessment, it is important to estimate its parameters α and β and the intensity function λ(t). In this paper, classical parametric regression methods have been utilized in the estimation of the parameters α and β, the intensity function and the mean time between failures of the Goel-Okumoto software reliability model. The parameters α and β and the mean time between failures (MTBF) of the Goel-Okumoto software model have been estimated using the maximum likelihood estimation (MLE) method, regression approach applied to the model and simple linear regression model without assuming the Goel-Okumoto model. When these three estimation methods were validated using root mean squared error (RMSE) and mean absolute value difference (MAVD), which are the common error measurement criteria, regression approach applied to the Goel-Okumoto model outperformed MLE and simple linear regression estimation methods. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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