Semantische Suche
Freitag, 31. März 2023, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}
| Vortragende(r) | Tim Schilpp |
|---|---|
| Titel | Analyzing Different Approaches to Integrating Handwritten and Generated Object-oriented Code |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Erik Burger |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Generating source code from models is one of the major advantages of a model-driven development process but most of the time this generated code does not suffice and developers are still required to write code by hand. This leads to the question of how to best integrate handwritten and generated code.
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. |
| Vortragende(r) | Kaan Berk Yaman |
|---|---|
| Titel | The Kconfig Variability Framework as a Feature Model |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Christopher Gerking |
| Vortragsmodus | in Präsenz |
| Kurzfassung | The Kconfig variability framework is used to develop highly variable software such as the Linux kernel, ZephyrOS and NuttX. Kconfig allows developers to break down their software in modules and define the dependencies between these modules, so that when a concrete configuration is created, the semantic dependencies between the selected modules are fulfilled, ensuring that the resulting software product can function. Kconfig has often been described as a tool of define software product lines (SPLs), which often occur within the context of feature-oriented programming (FOP). In this paper, we introduce methods to transform Kconfig files into feature models so that the semantics of the model defined in a Kconfig file are preserved. The resulting feature models can be viewed with FeatureIDE, which allows the further analysis of the Kconfig file, such as the detection of redundant dependencies and cyclic dependencies. |
Freitag, 31. März 2023, 11:30 Uhr
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Ort: Raum 333 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}
| Vortragende(r) | Tobias Telge |
|---|---|
| Titel | Automatisierte Gewinnung von Nachverfolgbarkeitsverbindungen zwischen Softwarearchitektur und Quelltext |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Jan Keim |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Nachverfolgbarkeitsverbindungen zwischen Architektur und Quelltext können das Wissen über ein System erweitern. Aufgrund des Erstellungsaufwands existieren in Softwareprojekten oft keine oder nur unvollständige Nachverfolgbarkeitsinformationen. Diese Arbeit untersucht einen Ansatz mit zwei Schritten, um automatisiert Nachverfolgbarkeitsverbindungen zwischen Architekturmodellelementen und Quelltext zu generieren. Damit die Erstellung von Nachverfolgbarkeitsverbindungen für verschiedene Programmiersprachen und Architektur-Metamodelle vereinheitlicht wird, werden im ersten Schritt aus den vorliegenden Artefakten Modelle erstellt. Der Quelltext wird dabei in ein von der konkreten Programmiersprache unabhängiges Modell überführt. Dafür wird ein Metamodell verwendet, das auf dem von der OMG spezifizierten KDM basiert. Für den zweiten Schritt werden auf den erstellten Modellen arbeitende Heuristiken und Aggregationen definiert. Diese werden genutzt, um die Nachverfolgbarkeitsverbindungen zu generieren. Die Heuristiken nutzen zum Beispiel Paket-, Pfad-, Namen- und Methoden-Informationen. Die Evaluation des Ansatzes nutzt einen dafür erstellten Goldstandard mit fünf Fallstudien. Es werden Nachverfolgbarkeitsverbindungen für PCM, UML, Java und Shell generiert. Für den Mikro-Durchschnitt des F1-Maßes wird ein Wert von 99,11 % erreicht. Fließt jede Komponente und Schnittstelle in gleichem Maße in den Wert ein, beträgt das F1-Maß 93,71 %. Insgesamt können mit dem Ansatz dieser Arbeit also sehr gute Ergebnisse erzielt werden. Für die TEAMMATES-Fallstudie wird mithilfe mehrerer Quelltextversionen der Einfluss der Konsistenz auf die Ergebnisse untersucht. Der Mikro-Durchschnitt des F1-Maßes ist für die konsistentere Version um 6,05 Prozentpunkte höher. Die Konsistenz kann also die Qualität der Ergebnisse beeinflussen. |
| Vortragende(r) | Ulas Uyanik |
|---|---|
| Titel | GUI-basiertes Testen einer Lernplattform-Anwendung durch Nutzung von Neuroevolution |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Daniel Zimmermann |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Software-Testing ist notwendig, um die Qualität und Funktionsfähigkeit von Softwareartefakten sicherzustellen. Es gibt sowohl automatisierte als auch manuelle Testverfahren. Allerdings sind automatisierte Verfahren, sowie menschliches Testen und skriptbasiertes Testen in Bezug auf Zeitaufwand und Kosten weniger gut skalierbar. Monkey-Testing, das durch zufällige Klicks auf der Benutzeroberfläche gekennzeichnet ist, berücksichtigt die Applikationslogik oft nicht ausreichend.
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. |
Freitag, 31. März 2023, 14:00 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}
| Vortragende(r) | Aaron Gätje |
|---|---|
| Titel | Efficient Training of Graph Neural Networks for Dynamic Phenomena (Proposal) |
| Vortragstyp | Proposal |
| Betreuer(in) | Daniel Ebi |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Graph Neural Networks (GNNs) have shown great potential for use cases that can be described as graphs. However, training GNNs presents unique challenges due to the characteristics of graph data. The focus of this thesis is to examine their learning abilities by developing a GNN-based surrogate model for the injection molding process from materials science. While numerical simulations can model the mold filling accurately, they are computationally expensive and require significant trial-and-error for parameter optimization. We propose representing the mold geometry as a static graph and constructing additional node and edge features from domain knowledge. We plan to enhance our model with a self-attention mechanism, allowing dynamic weighting of a node's neighbors based on their current states. Further improvements may come from customizing the model’s message passing function and exploring node sampling methods to reduce computational complexity. We compare our approach to conventional machine learning models w.r.t. predictive performance, generalizability to arbitrary mold geometries and computational efficiency.
This thesis is a follow-up work to a bachelor thesis written at the chair in 2022. |
| Vortragende(r) | Aleksandr Eismont |
|---|---|
| Titel | Surrogate Model Based Process Parameters Optimization of Textile Forming |
| Vortragstyp | Proposal |
| Betreuer(in) | Bela Böhnke |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Manufacturing optimization is crucial for organizations to remain competitive in the market. However, complex processes, such as textile forming, can be challenging to optimize, requiring significant resources. Surrogate-based optimization is an efficient method that uses simplified models to guide the search for optimal parameter combinations of manufacturing processes. Moreover, incorporating uncertainty estimates into the model can further speed up the optimization process, which can be achieved by using Bayesian deep neural networks. Additionally, convolutional neural networks can take advantage of spatial information in the images that are part of the textile forming parameters. In this work, a Bayesian deep convolutional surrogate model is proposed that uses all available process parameters to predict the shear angle of a textile element. By incorporating background information into the surrogate model, it is expected to predict detailed process results, leading to greater efficiency and increased product quality. |
Freitag, 14. April 2023, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}
| Vortragende(r) | Paul Giza |
|---|---|
| Titel | CGFLEX: A Flexible Framework for Causal Graph-based Data Synthesis |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Bela Böhnke |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Algorithms that extract dependencies from data and represent them as causal graphs must also be tested. For such tests, data with a known ground truth is required, but this is rarely available. Generating data under controlled conditions through simulations is expensive and time-consuming. A solution to this problem is to create synthetic datasets, where dependencies are predefined, to evaluate the results of these algorithms.
This work focuses on building a framework for the synthesis of data. In the framework, the synthesis process begins with generating a random dependency graph, specifically a directed acyclic graph. Each node in the graph, except the source nodes, has parent nodes and represents a variable. In the next step, each node is populated with predefined random dependencies. A dependency is a model that determines the value of a variable based on its parent variables. From this structure, datasets can be sampled. Users can control the properties of the causal graph through various parameters and choose from multiple types of dependencies, representing different complexity levels. Additionally, the sampling process allows for interactivity by enabling the exchange of dependencies during the sampling process. Dependencies can be exchanged with fixed values, probability distributions, or time series functions. This flexibility provides a robust tool for improving and comparing the mentioned algorithms under various conditions. |
Freitag, 28. April 2023, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: {{{Webkonferenzraum}}}
| Vortragende(r) | Hannes Greule |
|---|---|
| Titel | Evidence-based Token Abstraction for Software Plagiarism Detection |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Timur Sağlam |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Programming assignments for students are target of plagiarism. Especially for graded assignments, instructors want to detect plagiarism among the students. For larger courses, however, manual inspection of all submissions is a resourceful task. For this purpose, there are numerous tools that can help detect plagiarism in submissions. Many well-known plagiarism detection tools are token-based detectors. In an abstraction step, they map source code to a list of tokens, and such lists are then compared with each other. While there is much research in the area of comparison algorithms, the mapping is often only considered superficially. In this work, we conduct two experiments that address the issue of token abstraction. For that, we design different token abstractions and explain their differences. We then evaluate these abstractions using multiple datasets. We show that different abstractions have pros and cons, and that a higher abstraction level does not necessarily perform better. These findings are useful when adding support for new programming languages and for improving existing plagiarism detection tools. Furthermore, the results can be helpful to choose abstractions tailored to specific requirements. |
| Vortragende(r) | Jonas Strittmatter |
|---|---|
| Titel | Token-Based Plagiarism Detection for Statecharts |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Timur Sağlam |
| Vortragsmodus | in Präsenz |
| Kurzfassung | In the field of software engineering, existing plagiarism detection systems have primarily focused on detecting cases of plagiarism in code. However, other artefacts such as models also play a crucial role in the development process. Statecharts, in particular, are used to model the behavior of a system. This thesis investigates the applicability and challenges of applying token-based plagiarism detection systems to statecharts. We extend the plagiarism detector JPlag to support detecting cases of plagiarism in statecharts. Our approach is evaluated using a dataset of student assignments from a modeling course, where we generate plagiarized statecharts by adopting common obfuscation attacks. We study the effects of the token-extraction strategy, sorting techniques and the minimum token match parameter. The results suggest that an approach tailored to the specific kind of model, such as statecharts, works better than a generic solution for models. |