Abstract
Higher education courses teaching about agile software development (ASD) have increased in commonality as the ideas behind the Agile Manifesto became more commonplace in the industry. However, a lot of the literature on how ASD is applied in the classroom does not provide much actionable advice, focusing on frameworks or even moving beyond the software development area into teaching in an agile way. We, therefore, showcase early work on a pattern language that focuses on teaching ASD practices to university students, which stems from our own experiences as educators in higher education contexts. We present five patterns, specifically focused on team and project setup phase: Capping Team Size, Smaller Project Scope, Business Non-Critical Project, Self-assembling Teams, and Team Chooses Topic as a starting point for developing the overall pattern language. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Service-based architectures provide substantial benefits, yet service orchestration remains a challenge, particularly for newcomers. While various resources on orchestration techniques exist, they often lack clarity and standardization, making best practices difficult to implement and limiting their adoption within the software industry. To address this gap, we analyzed existing literature and tools to identify common orchestration practices. Based on our findings, we define three key orchestration resource optimization patterns: Preemptive Scheduling, Service Balancing, and Garbage Collection. Preemptive Scheduling allows the allocation of sufficient resources for services of higher priority in stressful situations, while Service Balancing enables a restructuring of the nodes to allow better resource usage. To end, Garbage Collection creates cleanup mechanisms to better understand the system’s resource usage and optimize it. These patterns serve as foundational elements for improving orchestration practices and fostering broader adoption in service-based architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Architectural patterns are frequently found in various software artifacts. The wide variety of patterns and their implementations makes detection challenging with current tools, especially since they often only support detecting patterns in artifacts written in a single language. Large Language Models (LLMs), trained on a diverse range of software artifacts and knowledge, might overcome the limitations of existing approaches. However, their true effectiveness and the factors influencing their performance have not yet been thoroughly examined. To better understand this, we developed MicroPAD. This tool utilizes GPT 5 nano to identify architectural patterns in software artifacts written in any language, based on natural-language pattern descriptions. We used MicroPAD to evaluate an LLM's ability to detect instances of architectural patterns, particularly infrastructure-related microservice patterns. To accomplish this, we selected a set of GitHub repositories and contacted their top contributors to create a new, human-annotated dataset of 190 repositories containing microservice architectural patterns. The results show that MicroPAD was capable of detecting pattern instances across multiple languages and artifact types. The detection performance varied across patterns (F1 scores ranging from 0.09 to 0.70), specifically in relation to their prevalence and the distinctiveness of the artifacts through which they manifest. We also found that patterns associated with recognizable, dominant artifacts were detected more reliably. Whether these findings generalize to other LLMs and tools is a promising direction for future research. © 2026 IEEE.