Advancing Software Resilience in the Age of LLM - Testing, Quality Assurance, and Metrics for Robust Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10125/112554

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    LLMs in Cybersecurity: Friend or Foe in the Human Decision Loop?
    (2026-01-06) Pekaric, Irdin; Mattson, Tom; Zech, Philipp
    Large Language Models (LLMs) are transforming human decision-making by acting as cognitive collaborators. Yet, this promise comes with a paradox: while LLMs can improve accuracy, they may also erode independent reasoning, promote over-reliance and homogenize decisions. In this paper,we investigate how LLMs shape human judgment insecurity-critical contexts. Through two exploratory focus groups (unaided and LLM-supported), we assess decision accuracy, behavioral resilience and reliance dynamics. Our findings reveal that while LLMs enhance accuracy and consistency in routine decisions, they can inadvertently reduce cognitive diversity and improve automation bias, which is especially the case among users with lower resilience. In contrast, high-resilience individuals leverage LLMs more effectively, suggesting that cognitive traits mediate AI benefit
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    Using Transformer and GAN Models for Software and Security Testing
    (2026-01-06) Hauswirth, Manfred; Huy, Christoph
    This paper investigates how Generative Adversarial Networks (GANs) and transformer models can support the process of software and security testing by generating and augmenting test data. We start with an analysis of the use of GANs for software testing, focusing on the generation of privacy-sensitive data in the automotive domain. We demonstrate that GANs can contribute to the efficient generation of test data which meet specific technical and regulatory requirements and discuss the limitations of applying differential privacy in this context. Based on this intermediate result, we investigate how lightweight open source transformer models can be applied to fuzzing to detect weaknesses. The evaluation is carried out using a modular training and evaluation framework. Our system implements the “Beyond Random Inputs” fuzzing approach by Rostami et al. (2024), using the Lua interpreter1as the fuzzing target. We then compare its effectiveness with the coverage-guided fuzzer AFL++ in terms of code coverage and vulnerability detection. Our results demonstrate the potential and limitations of transformer-based fuzzing in constrained environments, motivating further research on model scaling, resource efficiency, and domain transferability.
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