Software reliability model is the tool to measure the software reliability quantitatively. Hazard-Rate model is one of the most popular ones. The purpose of our research is to propose the hazard-rate model considering...Software reliability model is the tool to measure the software reliability quantitatively. Hazard-Rate model is one of the most popular ones. The purpose of our research is to propose the hazard-rate model considering fault level for Open Source Software (OSS). Moreover, we aim to adapt our proposed model to the hazard-rate considering the imperfect debugging environment. We have analyzed the trend of fault severity level by using fault data in Bug Tracking System (BTS) and proposed our model based on the result of analysis. Also, we have shown the numerical example for evaluating the performance of our proposed model. Furthermore, we have extended our proposed model to the hazard-rate considering the imperfect debugging environment and showed numerical example for evaluating the possibility of application. As the result, we found out that performance of our proposed model is better than typical hazard-rate models. Also, we verified the possibility of application of proposed model to hazard-rate model considering imperfect debugging.展开更多
The </span></span><span><span><span style="font-family:"">software reliability model is the stochastic model to measure the software <span>reliability quantitatively....The </span></span><span><span><span style="font-family:"">software reliability model is the stochastic model to measure the software <span>reliability quantitatively. A Hazard-Rate Model is </span></span></span></span><span><span><span style="font-family:"">the </span></span></span><span><span><span style="font-family:"">well</span></span></span><span><span><span style="font-family:"">-</span></span></span><span><span><span style="font-family:"">known one as the</span></span></span><span><span><span style="font-family:""> typical software reliability model. We propose Hazard-Rate Models Consider<span>ing Fault Severity Levels (CFSL) for Open Source Software (OSS). The purpose of </span><span>this research is to </span></span></span></span><span><span><span style="font-family:"">make </span></span></span><span><span><span style="font-family:"">the Hazard-Rate Model considering CFSL adapt to</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:"">baseline hazard function and 2 kinds of faults data in Bug Tracking System <span>(BTS)</span></span></span></span><span><span><span style="font-family:"">,</span></span></span><span><span><span style="font-family:""> <i>i.e.</i>, we use the covariate vectors in Cox proportional Hazard-Rate</span></span></span><span><span><span style="font-family:""> Model. Also, <span>we show the numerical examples by evaluating the performance of our pro</span><span>posed model. As the result, we compare the performance of our model with the</span> Hazard-Rate Model CFSL.展开更多
Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-spec...Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.展开更多
文摘Software reliability model is the tool to measure the software reliability quantitatively. Hazard-Rate model is one of the most popular ones. The purpose of our research is to propose the hazard-rate model considering fault level for Open Source Software (OSS). Moreover, we aim to adapt our proposed model to the hazard-rate considering the imperfect debugging environment. We have analyzed the trend of fault severity level by using fault data in Bug Tracking System (BTS) and proposed our model based on the result of analysis. Also, we have shown the numerical example for evaluating the performance of our proposed model. Furthermore, we have extended our proposed model to the hazard-rate considering the imperfect debugging environment and showed numerical example for evaluating the possibility of application. As the result, we found out that performance of our proposed model is better than typical hazard-rate models. Also, we verified the possibility of application of proposed model to hazard-rate model considering imperfect debugging.
文摘The </span></span><span><span><span style="font-family:"">software reliability model is the stochastic model to measure the software <span>reliability quantitatively. A Hazard-Rate Model is </span></span></span></span><span><span><span style="font-family:"">the </span></span></span><span><span><span style="font-family:"">well</span></span></span><span><span><span style="font-family:"">-</span></span></span><span><span><span style="font-family:"">known one as the</span></span></span><span><span><span style="font-family:""> typical software reliability model. We propose Hazard-Rate Models Consider<span>ing Fault Severity Levels (CFSL) for Open Source Software (OSS). The purpose of </span><span>this research is to </span></span></span></span><span><span><span style="font-family:"">make </span></span></span><span><span><span style="font-family:"">the Hazard-Rate Model considering CFSL adapt to</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:"">baseline hazard function and 2 kinds of faults data in Bug Tracking System <span>(BTS)</span></span></span></span><span><span><span style="font-family:"">,</span></span></span><span><span><span style="font-family:""> <i>i.e.</i>, we use the covariate vectors in Cox proportional Hazard-Rate</span></span></span><span><span><span style="font-family:""> Model. Also, <span>we show the numerical examples by evaluating the performance of our pro</span><span>posed model. As the result, we compare the performance of our model with the</span> Hazard-Rate Model CFSL.
文摘Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.
