The adoption of generative AI (GenAI) in software development practice has introduced significant changes for developers and organizations. However, for now the actual impact is still unknown and the changes to developer practices, behavior, and software quality are unclear. In my thesis, I explore this research area with the goal of contributing valuable insights for practitioners and researchers.

Time-Sensitive Networking (TSN) is a key technology for converged industrial communication systems enabling the integration of traffic with diverse Quality of Service (QoS) requirements such as timeliness, throughput, and reliability. To ensure that all data streams meet their requirements, the Network Calculus (NC) allows to analytically derive worst-case performance estimations. In this paper, we model rate-constrained streams alongside time-triggered hard real-time traffic in a line topology. Additionally, we compare the analytical results with a simulation conducted in the OMNeT++ network simulator. Our comparison shows that the worst-case delay predicted by the NC is 3 to 4 times higher than the delay observed in a simulation. Similarly, the estimated backlog (i.e., the amount of data queued in TSN switches) is $\mathbf{2}$ to $\mathbf{3}$ times larger than the simulation results.

With the integration of generative artificial intelligence (GenAI) tools such as GitHub Copilot into development processes, developers can be supported when writing code. As GitHub Copilot has a feature to provide up to ten solutions at once, we explore, how developers should approach those solutions with the goal of providing recommendations to achieve suitable trade-offs in finding correct solutions and checking solutions. In this study, we analyze a total of 2025 coding problems provided by LeetCode and 17 048 solutions to solve these problems generated by GitHub Copilot in Python. We focus on three key issues: firstly, whether it is beneficial to consider multiple solutions; secondly, the impact of the position of a solution; and thirdly, the number of solutions that should be checked by a developer. Overall, our results point to the following observations: (1) solutions are not less likely to be correct if they appear at later positions; (2) when looking for a solution to a common problem, checking four to five solutions is generally enough; (3) novel or difficult problems are unlikely to be solved by GitHub Copilot; (4) skipping the first solution is advised when considering only one solution, as the first solution is less likely to be correct; and (5) checking all solutions is necessary to not miss correct solutions, but the effort is usually not justified. Based on our study, we conclude that there is potential for improvement in better supporting developers. For instance, there are few cases where ten generated solutions provide more value than fewer solutions. Depending on the use scenario, it could be more useful if GitHub Copilot allowed developers to request a single, comprehensive solution.

Abstract Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of “modeler experience” (MX). MX includes factors such as user experience, motivation, integration, collaboration and versioning, and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.