Below, you will find current topics that can be worked in the context of a seminar, a Bachelor’s or a Master’s Thesis. The context indicates the scope of the work, and the keywords give you further information about the topic and its domain.
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Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. LLM Agents are a step further, they allow LLMs to use memory, tools and sequential thinking.
Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. Recent advancements try to make models think more, by either utilizing simple prompts or by training them using self-reflection via reinforcement learning.
The increasing adoption of continuous integration and continuous deployment (CI/CD) practices has transformed software development, with GitHub Actions playing a key role in automating workflows. Many projects rely on third-party GitHub Actions, which streamline deployment but also introduce security vulnerabilities due to outdated dependencies, excessive permissions, or lack of maintenance.
Despite the availability of security mechanisms such as Dependabot alerts and the GitHub Advisory Database, vulnerabilities often remain unpatched for long periods, leaving repositories exposed to supply chain attacks. Understanding how developers address, prioritize, or neglect these vulnerabilities is key to improving security practices in CI/CD environments.
Concurrent editing in cooperative software development often results in merge conflicts. Human effort is needed to resolve such merge conflicts. Studying merge conflicts and how to (semi-)automatically resolve them is an active research area.
Merge conflict resolution is a critical challenge in software development, particularly in large, collaborative projects that use version control systems like Git. When multiple developers modify the same part of a codebase, conflicts arise that require manual intervention. Existing automated resolution strategies often rely on rule-based approaches or traditional machine learning models, which struggle with complex and ambiguous cases. Deep Learning has the potential to improve conflict resolution by learning patterns from historical merge conflicts and predicting optimal resolution strategies. However, identifying the most effective Deep Learning architecture for this task remains an open question.