Browsing by Author "Hoorn, André van (Dr.-Ing.)"

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Open Access
Architecture-aware online failure prediction for software systems
(2018) Pitakrat, Teerat; Hoorn, André van (Dr.-Ing.)
Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. Existing online failure prediction approaches predict failures by observing the errors or the symptoms that indicate looming problems. This observable data is used to create models that can predict whether the system will transition into a failing state. However, these models usually represent the whole system as a monolith without considering their internal components. This thesis proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predictingthe failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduce the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component dependencies are combined into one model which employs Bayesian network theory to represent failure propagation. The combined model is continuously updated at runtime and makes predictions for individual components, as well as inferring their effects on other components and the whole system. The evaluation of component failure prediction is performed on three different experiments. The predictors are applied to predict component failures in a microservice-based application, critical events in Blue Gene/L supercomputer, and computer hard drive failures. The results show that the failures of individual components can be accurately predicted. The evaluation of the whole Hora approach is carried out on a microservice-based application. The results show that the Hora approach, which combines component failure prediction and architectural knowledge, can predict the component failures, their effects on other parts of the system, and the failures of the whole service. The Hora approach outperforms the monolithic approach that does not consider architectural knowledge and can improve the area under the Receiver Operating Characteristic (ROC) curve by 9.9%.
Open Access
Automated generation of tailored load tests for continuous software engineering
(2021) Schulz, Henning; Hoorn, André van (Dr.-Ing.)
Continuous software engineering (CSE) aims to produce high-quality software through frequent and automated releases of concurrently developed services. By replaying workloads that are representative of the production environment, load testing can identify quality degradation under realistic conditions. The literature proposes several approaches that extract representative workload models from recorded data. However, these approaches contradict CSE's high pace and automation in three aspects: they require manual parameterization, generate resource-intensive system-level load tests, and lack the means to select appropriate periods from the temporally varying production workload to justify time-consuming testing. This dissertation addresses the automated generation of tailored load tests to reduce the time and resources required for CSE-integrated testing. The tailoring needs to consider the services of interest and select the most relevant workload periods based on their context, such as the presence of a special sale when testing a webshop. Also, we intend to support experts and non-experts with a high degree of automation and abstraction. We develop and evaluate description languages, algorithms, and an automated load test generation approach that integrates workload model extraction, clustering, and forecasting. The evaluation comprises laboratory experiments, industrial case studies, an expert survey, and formal proofs. Our results show that representative context-tailored load tests can be generated by learning a workload model incrementally, enriching it with contextual information, and predicting the expected workload using time series forecasting. For further tailoring the load tests to services, we propose extracting call hierarchies from recorded invocation traces. Dedicated models of evolving manual parameterizations automate the generation process and restore the representativeness of the load tests. Furthermore, the integration of our approach with an automated execution framework enables load testing for non-experts. Following open-science practices, we provide supplementary material online. The proposed approach is a suitable solution for the described problem. Future work should refine specific building blocks the approach leverages. These blocks are the clustering and forecasting techniques from existing work, which we have assessed to be limited for predicting sharply fluctuating workloads, such as load spikes.