Semantische Suche

However, the already existing methods for time series monitoring tend to ignore the background information such as relationships between components or process structure that is available for almost any complex system. Such background information gives a context to the time series data, and can potentially improve the performance of time series monitoring tasks.

In this bachelor thesis, we show how to incorporate structured background information to improve three different time series monitoring tasks. We perform the experiments on the data from the cloud computing center, where we extract background information from system traces. Additionally, we investigate different representations and quality of background information and conclude that its usefulness is independent from a concrete time series monitoring task.

Es werden LSTMs, GRUs, CTRNNs und Elman Netze untersucht. Die Netze werden dabei untersucht sich einen Punkt zu merken und anschließend nach dem Punkt mit einem virtuellen Roboterarm zu greifen.

Bei LSTM, GRU und Elman Netzen wird auch untersucht wie die Netze die Aufgabe lösen, wenn jedes Neuron nur auf den eigenen Speicher zugreifen kann.

Dabei hat sich herausgestellt, dass LSTMs und GRUs deutlich besser bei den Experimenten bewertet werden als CTRNNs und Elman Netze. Außerdem werden die Rechenzeit und der Zusammenhang zwischen der Anzahl der zu trainierenden Parameter und der Ergebnisse der Experimente verglichen.

With this thesis, we propose PeReDeS, an approach that integrates into the development cycle of modern software projects, and explicitly models an automated performance regression detection system that provides feedback quickly and reduces manual effort for setup and load test analysis. PeReDeS is embedded into pipelines for continuous integration, manages the load test execution and lifecycle, processes load test results and makes feedback available to the authoring developer via reports on the coding platform. We further propose a method for detecting deviations in performance on load test results, based on Welch's t-test. The method is adapted to suit the context of performance regression detection, and is integrated into the PeReDeS detection pipeline. We further implemented our approach and evaluated it with an user study and a data-driven study to evaluate the usability and accuracy of our method.

This thesis evaluates the quality of estimated change sequences in the context of model consistency preservation. To derive such sequences, matching elements across the compared models need to be identified and their differences need to be computed. We evaluate a sequence derivation strategy that matches elements based on their unique identifier and one that establishes a similarity metric between elements based on the elements’ features. As an evaluation baseline, different test suites are created. Each test consists of an initial and changed version of both a UML class diagram and consistent Java source code. Using the different strategies, we derive and propagate change sequences based on the state-based difference of the UML view and evaluate the outcome in both domains. The results show that the identity-based matching strategy is able to derive the correct change sequence in almost all (97 %) of the considered cases. For the similarity-based matching strategy we identify two reoccurring error patterns across different test suites. To address these patterns, we provide an extended similarity-based matching strategy that is able to reduce the occurrence frequency of the error patterns while introducing almost no performance overhead.