Semantische Suche

Freitag, 31. März 2023, 11:30 Uhr

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Ort: Raum 333 (Gebäude 50.34)

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: https://kit-lecture.zoom.us/j/67744231815

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)

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: https://sdq.kastel.kit.edu/wiki/SDQ-Oberseminar/Microsoft_Teams

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.

Freitag, 5. Mai 2023, 11:30 Uhr

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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: https://sdq.kastel.kit.edu/wiki/SDQ-Oberseminar/Microsoft_Teams

Vortragende(r) Lukas Burgey
Titel Continuous Integration of Performance Models for Lua-Based Sensor Applications
Vortragstyp Masterarbeit
Betreuer(in) Manar Mazkatli
Vortragsmodus in Präsenz
Kurzfassung Architecture-level performance models of software like the PCM can aid with the development of the software by preventing architecture degradation and helping to diagnose performance issues during the implementation phase.

Previously, manual intervention was required to create and update such models. The CIPM approach can be employed to automatically make a calibrated PCM instance available during the development of software. A prototypical implementation of the CIPM approach targets microservice-based web applications implemented in Java. No implementations for other programming languages exist and the process of adapting the CIPM approach to support another programming language has previously not been explored.

We present an approach to adapting CIPM to support Lua-based sensor applications. A prototypical implementation of the adapted approach was evaluated using real-world Lua-based sensor applications from the SICK AppSpace ecosystem. The evaluation demonstrates the feasibility of the adapted approach, but also reveals minor technical issues with the implementation.

Vortragende(r) Moritz Brödel
Titel Preventing Automatic Code Plagiarism Generation Through Token String Normalization
Vortragstyp Bachelorarbeit
Betreuer(in) Timur Sağlam
Vortragsmodus in Präsenz
Kurzfassung Code plagiarism is a significant problem in computer science education. Token-based plagiarism detectors, which represent the state-of-the-art in code plagiarism detection, excel at identifying manually plagiarized submissions. Unfortunately, they are vulnerable to automatic plagiarism generation, particularly when statements are inserted or reordered. Therefore, this thesis introduces token string normalization, which makes the results of token-based plagiarism detectors invariant to statement insertion and reordering. It inher- its token-based plagiarism detectors’ high language independence and utilizes a program graph. We integrate token string normalization into the state-of-the-art token-based plagiarism detector JPlag. We show that this prevents automatic plagiarism generation using statement insertion and reordering. Additionally, we confirm that JPlag’s existing capabilities are retained.
Vortragende(r) Alp Toraç Genç
Titel Prototypical implementation of discrete-event-based co-simulation of hardware and software
Vortragstyp Bachelorarbeit
Betreuer(in) Sebastian Weber
Vortragsmodus in Präsenz
Kurzfassung Computer-supported simulations provide multiple ways to analyse design decisions and avoid many possible mistakes. For simulating large and complex systems, multiple simulation tools may be necessary, as having the means to simulate in only one tool may not be the best approach. In such cases, co-simulation can be used to simulate the said system by interconnecting the mentioned simulation tools using a co-simulation standard. A system that consists of hardware and software falls under this category of systems.

Depending on how a system is to be co-simulated, choosing a co-simulation standard can be challenging, as there are many factors and trade-offs to consider. In this thesis, existing co-simulation standards with discrete-event-based co-simulation support will be researched and compared to one another. This comparison will then be used to choose a co-simulation standard for an exemplary case of hardware-software co-simulation, which will be prototypically implemented and evaluated.