Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. The models being used in the literature are mostly based on Markov chains. They are used to generate test cases that are statistically close to what the applica-tion is expected to receive when in production. In this thesis, we propose a model for reli-ability testing that is created directly from the log file of a web application. Our proposed model is also based on Markov chains and has two components: one component, based on a modified tree, captures the most frequent behaviors, while the other component is another Markov chain that captures infrequent behaviors. The result is a statistically cor-rect model that shows clearly what most users do on the site. The thesis also presents an evaluation method for estimating the accuracy of vari-ous reliability-testing usage models. The method is based on comparison between ob-served users’ traces and traces inferred from the usage model. Our method gauges the accuracy of the reliability-testing usage model by calculating the sum of goodness-of-fit values of each traces and scaling the result between 0 and 1. Finally, we present an experimental study on the log of a real web site and discuss the way to use proposed usage model to generate test sequences, as well as strength and weakness of the model for reliability testing.

Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. The models being used in the literature are mostly based on Markov chains. They are used to generate test cases that are statistically close to what the applica-tion is expected to receive when in production. In this thesis, we propose a model for reli-ability testing that is created directly from the log file of a web application. Our proposed model is also based on Markov chains and has two components: one component, based on a modified tree, captures the most frequent behaviors, while the other component is another Markov chain that captures infrequent behaviors. The result is a statistically cor-rect model that shows clearly what most users do on the site. The thesis also presents an evaluation method for estimating the accuracy of vari-ous reliability-testing usage models. The method is based on comparison between ob-served users’ traces and traces inferred from the usage model. Our method gauges the accuracy of the reliability-testing usage model by calculating the sum of goodness-of-fit values of each traces and scaling the result between 0 and 1. Finally, we present an experimental study on the log of a real web site and discuss the way to use proposed usage model to generate test sequences, as well as strength and weakness of the model for reliability testing.

Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. The models being used in the literature are mostly based on Markov chains. They are used to generate test cases that are statistically close to what the applica-tion is expected to receive when in production. In this thesis, we propose a model for reli-ability testing that is created directly from the log file of a web application. Our proposed model is also based on Markov chains and has two components: one component, based on a modified tree, captures the most frequent behaviors, while the other component is another Markov chain that captures infrequent behaviors. The result is a statistically cor-rect model that shows clearly what most users do on the site. The thesis also presents an evaluation method for estimating the accuracy of vari-ous reliability-testing usage models. The method is based on comparison between ob-served users’ traces and traces inferred from the usage model. Our method gauges the accuracy of the reliability-testing usage model by calculating the sum of goodness-of-fit values of each traces and scaling the result between 0 and 1. Finally, we present an experimental study on the log of a real web site and discuss the way to use proposed usage model to generate test sequences, as well as strength and weakness of the model for reliability testing.

Knowledge of the resource usage of applications and the resource usage capacity of hardware platforms is essential when developing a system. The resource usage must not over exceed the capacity of a platform, as it could otherwise fail to meet its real-time constraints due to resource shortages. Furthermore, it is beneficial from a cost-effectiveness stand-point that a hardware platform is not under-utilised by systems software. This thesis examines two systems aspects: the hardware resource usage of applications and the resource capacity of hardware platforms, defined as the capacity of each resource included in a hardware platform. Both of these systems aspects are investigated and modelled using a black box perspective since the focus is on observing the online usage and capacity. Investigating and modelling these two approaches is a crucial step towards defining and constructing hardware and software models. We evaluate regressive and auto-regressive modelling approaches of modelling CPU, L2 cache and L3 cache usage of applications. The conclusion is that first-order autoregressive and Multivariate Adaptive Regression Splines show promise of being able to model resource usage. The primary limitation of both modelling approaches is their inability to model resource usage when it is highly irregular. The capacity models of CPU, L2 and L3 cache derived by exerting heavy workloads onto a test platform shows to hold against a real-life application concerning L2 and L3 cache capacity. However, the CPU usage model underestimates the test platform's capacity since the real-life application over-exceeds the theoretical maximum usage defined by the model.

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