Semantische Suche

However, some domains, such as Cloud Systems Monitoring, lack readily available causal domain knowledge, and thus the knowledge must be approximated. Therefore, it is important to have a systematic investigation of the processes and design decision that affect the knowledge generation process.

In this study, we showed how causal discovery algorithms can be employed to generate causal domain knowledge from raw textual logs in the Cloud Systems Monitoring domain. We also investigated the impact of various design choices on the domain knowledge generation process through systematic testing across multiple datasets and shared the insights we gained. To our knowledge, this is the first time such an investigation has been conducted.

over, such as environmental impacts. Often, they use scenarios in their decision making process. Scenarios provide a common and intuitive way to communicate and characterize different uncertain outcomes in many decision support applications, especially in broad public debates. However, they often fall short of their potential, particularly when applied for groups with diverse interests and worldviews, due to the difficulty of choosing a small number of scenarios to summarize the entire range of uncertain future outcomes. Scenario discovery addresses these problems by using statistical or data-mining algorithms to find easy-to-interpret, policy-relevant regions in the space of uncertain input parameters of computer simulation models. One of many approaches to scenario discovery is subgroup discovery, an approach from the domain of explainable Artificial Intelligence.

In this thesis, we test and evaluate multiple different subgroup discovery methods for their applicabilty to scenario discovery applications.

In this thesis, we propose a novel change detector, RADMAN, which leverages the random Fourier feature-based kernel approximation to efficiently detect changes in data streams with a polylogarithmic runtime complexity of O(log^2 n) per insert operation, with n the total number of observations. The proposed approach runs significantly faster than existing methods but obtains similar result quality. Our experiments on synthetic and real-world data sets show that it performs better than current state-of-the-art approaches.

Previous authors suggested a number of possible solutions to this problem of integrating handwritten and generated code but the possibilities to objectively compare these alternatives are still limited. Therefore we collected the different analysis criteria suggested by other authors as well as complemented them with additional criteria proposed by senior developers. We then applied these criteria to the possible integration approaches presented by previous authors to create an overview for developers to use when having to choose an integration approach for their model-driven project.

Applying the results of this analysis we chose the best-fitting integration approach for the development of a large industrial development project and found out that migrating to this suggested integration approach would improve the overall software quality regarding complexity, coupling, and cohesion.

Der Fokus dieser Bachelorarbeit liegt auf dem automatisierten neuroevolutionären Testverfahren, das neuronale Netze als Testagenten verwendet und sie mithilfe evolutionärer Algorithmen über mehrere Generationen hinweg verbessert. Um das Training der Agenten zu ermöglichen und den Vergleich zum Monkey-Testing zu ermöglichen, wurde eine simulierte Version der Lernplattform Anki implementiert. Zur Beurteilung der Testagenten wurde eine Belohnungsstruktur in der simulierten Anwendung entwickelt. Die Ergebnisse zeigen, dass das neuroevolutionäre Testverfahren im Vergleich zum Monkey-Testing in Bezug auf erreichte Belohnungen signifikant besser abschneidet. Dadurch wird die Applikationslogik im Testprozess besser berücksichtigt.