Hier finden Sie Themenbereiche für Abschlussarbeiten die am Lehrstuhl Softwaresysteme betreut werden. Wir freuen uns über Ihre eigenen Themenvorschläge, suchen aber auch mit Ihnen eine geeignete Abschlussarbeit in einem der unten stehenden Themenfelder.

Wenn Sie sich für eine Abschlussarbeit am Lehrstuhl Softwaresysteme interessieren, senden Sie eine Email mit Betreff „Anfrage Abschlussarbeit“ und den folgenden Informationen an Annemarie Wittig >Mail<:

  • Studiengang,
  • Termin an dem Sie mit der Arbeit beginnen möchten,
  • Themenvorschlag und/oder Themenbereich(e) am Lehrstuhl Softwaresysteme für die Sie sich interessieren und
  • Eine Liste mit Modulen des Wahl(pflicht)bereichs, die Sie belegt haben und die Sie für die gewählten Themenbereiche als relevant erachten.

Die Themenbereiche, in denen es aktuell noch Betreuungskapazitäten gibt, sind unten aufgelistet.

Aktuell gibt es für das Wintersemester 2025/2026 nur noch geringe Betreuungskapazitäten.

Themenfelder

LLM-based tools, such as Copilot, ChatGPT, or CodeWhisperer, often promise to help developers build software more efficiently and effectively. However, the specific impact these tools have on the development processes of individuals and teams remain unclear. In this topic area, a thesis could investigate questions focusing on productivity, decision making, team communication, developers‘ behaviors and emotions, or adoption barriers faced when introducing LLM-based tools.

Example Research Questions:

  • How does the introduction of an LLM-based assistant change different productivity metrics of individuals and teams?
  • How do programming beginners interact with LLM-based chatbots?

In contrast to traditional software, ML software is highly data-dependent and requires continuous monitoring and updating to maintain performance. The unpredictable and experimental nature of data and model behavior adds complexity to development and maintenance. Additionally, new roles with diverse skill sets are driving these products, adding complexity to processes and organizational structures. A thesis in this topic could focus on technical, social, or ethical aspects of engineering ML-enabled systems, thus developing tools to solve a specific task, or reviewing (gray) literature, conducting interview or survey studies to understand specific challenges or best practices. Currently, we are specifically interested in software systems containing or interacting with an LLM. Nevertheless, if you have a thesis in mind related to “traditional„ ML, please approach us.

Example Research Questions:

  • What tools exist to support developers when prompt engineering and how do they align with developers‘ needs?

Modern software development involves a plethora of technologies, including build tools, code frameworks, databases, CI/CD pipelines and containerization technologies. All of these technologies encode not only hundreds of configuration options in their own syntax, semantics and structure, but also introduce non-obvious configuration dependencies across the used technology stack. Unfortunately, there is no complete overview of the intertwined configuration dependencies. The lack of an overview and the rapid evolution of software systems inevitably lead to misconfigurations, which often infiltrate a software project unnoticed. The resulting misconfigurations, such as inconsistent configurations, are complex, far-reaching and much more difficult to detect and resolve than typical software errors. In this area, a thesis could investigate topics that focus on dependency detection, misconfiguration resolution, dependency validation, configuration space evolution and maintenance, and the dimension of software configuration in ML-enabled systems.

Example Research Questions:

  • How do configuration dependencies manifest in ML-enabled software systems?
  • Can LLMs extract configuration dependencies from Stack Overflow posts?

Performance models for configurable software systems are no longer just about producing accurate estimates — they must also convey how reliable those estimates are. Recent techniques like conformal prediction provide lightweight, model-agnostic uncertainty estimates, but many questions remain about how to best use this information in practice.

A thesis in this area could go beyond implementing prediction methods to explore new ways uncertainty quantification can support engineering tasks. This includes guiding sampling strategies, improving configuration optimization, exposing blind spots in data collection, or increasing developer trust through interpretability and visualization.

Example Research Questions:

  • How can uncertainty estimates improve sampling or active learning in performance modeling?
  • How do developers interpret and act on uncertainty information when tuning configurations?

Wir sind offen für eine Betreuung von Themen in Firmen, sofern es thematisch passt. Eine Geheimhaltungsklausel (NDA) wird hierbei explizit ausgeschlossen!

Abgeschlossene Arbeiten

Managing Prompt Engineering Experiments: Tools and User’s Needs (Bachelor)

Socio-Technical Challenges in Software Engineering (Bachelor)

Evolution of the Configuration Space of Open-Source Software Systems (Bachelor)

Ermittlung von Konfigurationsoptionen im Source Code mit Fokus auf Machine Learning Bibliotheken in Python (Bachelor)

Tracking Configuration Options on Source Code Focusing on Java Frameworks (Bachelor)

Klassifikation von Konfigurationsabhängigkeiten zwischen verschiedenen Technologien (Bachelor)

Software Engineering 4 AI in der Praxis (Bachelor)

Abdeckungsanalyse von konfigurierbaren Performance-Benchmarks und Softwaresystemen (Bachelor/Master)

Energy Consumption in Practice (Bachelor)

Studie über Konfigurationsfehler (Bachelor)

Automatisiertes Beheben von Konfigurationsfehlern (Bachelor/Master)

Gruppenbasierte Sensitivitätsanalyse für die Vorhersage nicht-funktionaler Eigenschaften konfigurierbar Softwaresysteme (Bachelor)

Group Sampling for Learning Configuration-specific Software Performance (Bachelor)