Semantische Suche

Bezüglich der Kompression von Informationen über Benutzeroberflächen durch die Autoencoder waren die Ergebnisse dagegen vielversprechend, da die Testdaten auch in hoher Auflösung von 900 x 935 Pixeln mit hoher Genauigkeit rekonstruiert werden konnten. Erste Experimente ergaben, dass die Autoencoder darüber hinaus Fähigkeiten entwickeln, Applikationen mit ähnlichem Farbschema oder ähnlicher Designsprache zu kodieren und wiederzugeben. Ein erstes Fazit über die Fähigkeiten zur Generalisierung fällt daher ebenso positiv aus. Die Genauigkeit der Reproduktion sinkt, wenn die Eingabe farblich oder designtechnisch stark von den Trainingsdaten abweicht.

In dieser Arbeit implementieren wir eine bidirektionale Modell-zu-Text-Transformation, welche die als XML-Dateien persistierten Zustände der FeatureIDE-Modelle in geeignete Modellrepräsentationen überführt, um daraus feingranulare Änderungssequenzen abzuleiten. Diese können zur deltabasierten Konsistenzerhaltung verwendet werden. Für die Modellrepräsentation verwenden wir ein bestehendes Metamodell für Variabilität. Zur Ableitung der Änderungssequenzen wird ein existierendes Konsistenzerhaltungsframework eingesetzt. Wir validieren die Korrektheit der Transformation mithilfe von Round-Trip-Tests. Dabei zeigen wir, dass die in dieser Arbeit implementierte Transformation alle geteilten Informationen zwischen FeatureIDE und dem Variabilitäts-Metamodell korrekt transformiert. Zudem können mithilfe der in dieser Arbeit implementierten Transformation und mit dem verwendeten Konsistenzerhatlungsframework zu 94,44% korrekte feingranulare Änderungssequenzen aus den als XML-Datei persistierten Zuständen der FeatureIDE-Modelle abgeleitet werden.

MCDE quantifies dependencies as the average discrepancy between marginal and conditional distributions. In practice, this value is approximated with a dependency estimator. However, the original implementation of this estimator converges rather slowly, which leads to suboptimal results in terms of statistical power. Moreover, MCDE is only able to quantify dependencies among univariate random variables, but not multivariate ones. In this thesis, we make 2 major improvements to MCDE. First, we propose 4 new dependency estimators with faster convergence. We show that MCDE equipped with these new estimators achieves higher statistical power. Second, we generalize MCDE to GMCDE (Generalized Monte Carlo Dependency Estimation) to quantify dependencies among multivariate random variables. We show that GMCDE inherits all the desirable properties of MCDE and demonstrate its superiority against the state-of-the-art dependency measures with experiments.

In this thesis, we investigated the automatic classification of scientific papers in the Software Engineering domain. In doing so, a classification scheme of paper contents with regard to Research Object, Statement, and Evidence was consolidated. We then investigate in a comparative analysis the machine learning algorithms Naïve Bayes, Support Vector Machine, Multi-Layer Perceptron, Logistic Regression, Decision Tree, and BERT applied to the classification task.

There are multiple examples that highlight the importance of dependency estimation, like knowing there exists a correlation between the regular dose of a drug and the health of a patient helps to understand the impact of a newly manufactured drug. Knowing how the case material, brand, and condition of a watch influences the price on an online marketplace can help to buy watches at a good price. Material sciences can also use dependency estimation to predict many properties of a material before it is synthesized in the lab, so fewer experiments are necessary.

Many dependency estimation algorithms require a large amount of data for a good estimation. But data can be expensive, as an example experiments in material sciences, consume material and take time and energy. As we have the challenge of expensive data collection, algorithms need to be data efficient. But there is a trade-off between the amount of data and the quality of the estimation. With a lack of data comes an uncertainty of the estimation. However, the algorithms do not always quantify this uncertainty. As a result, we do not know if we can rely on the estimation or if we need more data for an accurate estimation.

In this bachelor's thesis we compare different state-of-the-art dependency estimation algorithms using a list of criteria addressing these challenges and more. We partly developed the criteria our self as well as took them from relevant publications. The existing publications formulated many of the criteria only qualitative, part of this thesis is to make these criteria measurable quantitative, where possible, and come up with a systematic approach of comparison for the rest.

From 14 selected criteria, we focus on criteria concerning data efficiency and uncertainty estimation, because they are essential for lowering the cost of dependency estimation, but we will also check other criteria relevant for the application of algorithms. As a result, we will rank the algorithms in the different aspects given by the criteria, and thereby identify potential for improvement of the current algorithms.

We do this in two steps, first we check general criteria in a qualitative analysis. For this we check if the algorithm is capable of guided sampling, if it is an anytime algorithm and if it uses incremental computation to enable early stopping, which all leads to more data efficiency.

We also conduct a quantitative analysis on well-established and representative datasets for the dependency estimation algorithms, that performed well in the qualitative analysis. In these experiments we evaluate more criteria: The robustness, which is necessary for error-prone data, the efficiency which saves time in the computation, the convergence which guarantees we get an accurate estimation with enough data, and consistency which ensures we can rely on an estimation.