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Freitag, 3. Juni 2022, 11:30 Uhr

Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}

Vortragende(r) Tobias Haßberg
Titel Development of an Active Learning Approach for One Class Classifi cation using Bayesian Uncertainty
Vortragstyp Masterarbeit
Betreuer(in) Bela Böhnke
Vortragsmodus in Präsenz
Kurzfassung In One-Class classification, the classifier decides if points belong to a specific class. In this thesis, we propose an One-Class classification approach, suitable for active learning, that models for each point, a prediction range in which the model assumes the points state to be. The proposed classifier uses a Gaussian process. We use the Gaussian processes prediction range to derive a certainty measure, that considers the available labeled points for stating its certainty. We compared this approach against baseline classifiers and show the correlation between the classifier's uncertainty and misclassification ratio.

Freitag, 24. Juni 2022, 11:30 Uhr

Ort: MS Teams
Webkonferenz: {{{Webkonferenzraum}}}

Vortragende(r) Kevin Werber
Titel Assessing Word Similarity Metrics For Traceability Link Recovery
Vortragstyp Bachelorarbeit
Betreuer(in) Jan Keim
Vortragsmodus online
Kurzfassung The software development process usually involves different artifacts that each describe different parts of the whole software system. Traceability Link Recovery is a technique that aids the development process by establishing relationships between related parts from different artifacts. Artifacts that are expressed in natural language are more difficult for machines to understand and therefore pose a challenge to this link recovery process. A common approach to link elements from different artifacts is to identify similar words using word similarity measures. ArDoCo is a tool that uses word similarity measures to recover trace links between natural language software architecture documentation and formal architectural models. This thesis assesses the effect of different word similarity measures on ArDoCo. The measures are evaluated using multiple case studies. Precision, recall, and encountered challenges for the different measures are reported as part of the evaluation.

Freitag, 24. Juni 2022, 11:30 Uhr

Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}

Vortragende(r) Tobias Hombücher
Titel Generalized Monte Carlo Dependency Estimation and Anytime Supervised Filter Feature Selection
Vortragstyp Masterarbeit
Betreuer(in) Edouard Fouché
Vortragsmodus online
Kurzfassung Dependency estimation is an important problem in statistics and is applied frequently in data science. As modern datasets can be very large, dependency estimators should be efficient and leverage as much information from data as possible. Traditional bivariate and multivariate dependency estimators are only capable to estimate dependency between two or n one-dimensional datasets, respectively. In this thesis, we are interested in how to develop estimators that can estimate the dependency between n multidimensional datasets, which we call "generalized dependency estimators".

We extend the recently introduced methodology of Monte Carlo Dependency Estimation (MCDE), an effective and efficient traditional multivariate dependency estimator. We introduce Generalized Monte Carlo Dependency Estimation (gMCDE) and focus in particular on the highly relevant subproblem of generalized dependency estimation, known as canonical dependency estimation, which aims to estimate the dependency between two multidimensional datasets. We demonstrate the practical relevance of Canonical Monte Carlo Dependency Estimation (cMCDE) by applying it to feature selection, introducing two methodologies for anytime supervised filter feature selection, Canonical Monte Carlo Feature Selection (cMCFS) and Canonical Multi Armed Bandit Feature Selection (cMABFS). cMCFS directly applies the methodology of cMCDE to feature selection, while cMABFS treats the feature selection problem as a multi armed bandit problem, which utilizes cMCDE to determine relevant features.

Vortragende(r) Jonas Zoll
Titel Injection Molding Simulation based on Graph Neural Networks (GNNs)
Vortragstyp Bachelorarbeit
Betreuer(in) Daniel Ebi
Vortragsmodus in Präsenz
Kurzfassung Numerical filling simulations are an important tool for the development of injection molding parts. Existing simulations rely on numerical solvers based on the finite element method. These solvers are reliable and precise, but very computationally expensive even on simple part geometries.

In this thesis, we aim to develop a faster injection molding simulation based on Graph Neural Networks (GNNs) as a surrogate model. Our approach learns a simulation as a composition of three functions: an encoder, a processor and a decoder. The encoder takes in a graph representation of a 3D geometry of an injection molding part and returns a numeric embedding of each node in the graph. The processor updates the embeddings of each node multiple times based on its neighbors. The decoder then decodes the final embeddings of each node into physically meaningful variables, say, the fill state of the node. Our model can predict the progression of the flow front during a time step with a fixed size. To simulate a full mold filling process, our model is applied sequentially until the entire mold is filled. Our architecture is applicable to any kind of material, geometry and injection process parameters. We evaluate our architecture by its accuracy and runtime when predicting node properties. We also evaluate our models transfer learning ability on a real world injection molding part.

Vortragende(r) Mingzhe Tao
Titel Meta-learning for Encoder Selection
Vortragstyp Proposal
Betreuer(in) Federico Matteucci
Vortragsmodus in Präsenz
Kurzfassung In the real world, mixed-type data is commonly used, which means it contains both categorical and numerical data. However, most algorithms can only learn from numerical data. This makes the selection of encoder becoming very important. In this presentation, I will present an approach by using ideas from meta-learning to predict the performance from the meta-features and encoders.

Freitag, 1. Juli 2022, 11:30 Uhr

Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}

Vortragende(r) Anne-Kathrin Hermann
Titel Bewertung der Qualität von Low-Code-Programmen für Datenfluss-Beschreibungen
Vortragstyp Bachelorarbeit
Betreuer(in) Thomas Kühn
Vortragsmodus in Präsenz
Kurzfassung Das Messen der Qualität von Datenfluss-Low-Code-Programmen und auch das Erstellen qualitativ hochwertiger Programme ist schwer. Es entstehen viele Programme mit Anzeichen für schlechte Qualität, die zwar Ergebnisse liefern, aber schlecht wartbar und unverständlich sind. Im Laufe dieser Arbeit wurde die Übertragbarkeit, von klassischen Codemetriken und Graphmetriken überprüft und durchgeführt, um zu evaluieren, welche Metriken sich für die Messung der Qualität von Low-Code-Programmen eignen?
Vortragende(r) Quang Dao
Titel Coreference Resolution for Software Architecture Documentation
Vortragstyp Bachelorarbeit
Betreuer(in) Jan Keim
Vortragsmodus in Präsenz
Kurzfassung In software engineering, software architecture documentation plays an important role. It contains many essential information regarding reasoning and design decisions. Therefore, many activities are proposed to deal with documentation for various reasons, e.g., extract- ing information or keeping different forms of documentation consistent. These activities often involve automatic processing of documentation, for example traceability link recovery (TLR). However, there can be problems for automatic processing when coreferences are present in documentation. A coreference occurs when two or more mentions refer to the same entity. These mentions can be different and create ambiguities, for example when there are pronouns. To overcome this problem, this thesis proposes two contributions to resolve coreferences in software architecture documentation.

The first contribution is to explore the performance of existing coreference resolution models for software architecture documentation. The second is to divide coreference resolution into many more specific type of resolutions, like pronoun resolution, abbreviation resolution, etc.

Freitag, 22. Juli 2022, 11:30 Uhr

Ort: MS Teams
Webkonferenz: {{{Webkonferenzraum}}}

Vortragende(r) Philipp Uhrich
Titel Empirical Identification of Performance Influences of Configuration Options in High-Performance Applications
Vortragstyp Masterarbeit
Betreuer(in) Larissa Schmid
Vortragsmodus online
Kurzfassung Many modern high-performance applications are highly-configurable software systems that provide hundreds or even thousands of configuration options. System administrators or application users need to understand all these options and their impacts on the software performance to choose suitable configuration values. To understand the influence of configuration options on the run-time characteristics of a software system, users can use performance prediction models, but building performance prediction models for highly-configurable high-performance applications is expensive. However, not all configuration options, which a software system offers, are performance-relevant. Removing these performance-irrelevant configuration options from the modeling process can reduce the construction cost. In this thesis, we explore and analyze two different approaches to empirically identify configuration options that are not performance-relevant and can be removed from the performance prediction model. The first approach reuses existing performance modeling methods to create much cheaper prediction models by using fewer samples and then analyzing the models to identify performance-irrelevant configuration options. The second approach uses white-box knowledge acquired through dynamic taint analysis to systematically construct the minimal number of required experiments to detect performance-irrelevant configuration options. In the evaluation with a case study, we show that the first approach identifies performance-irrelevant configuration options but also produces misclassifications. The second approach did not perform to our expectations. Further improvement is necessary.