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Abstract: Large Language Models (LLM) have shown stunning abilities to carry out tasks that were previously conducted by humans. The future role of humans and the responsibilities assigned to non-human LLMs affect society fundamentally. In that context, LLMs have often been compared to humans. However, it is surprisingly difficult to make a fair empirical comparison between humans and LLMs. To address those...
Abstract: A number of challenging aspects have to be considered, when the Internet of Things (IoT) and Artificial Intelligence (AI) are combined into intelligent IoT systems. A key aspect that demands high attention is trustworthiness. As part of the investigations we conduct in this area in collaboration with partner companies, the need of a holistic view for trustworthiness in Intelligent IoT systems has ...
Abstract: In our AI projects with machinery and plant engineering customers, we encounter recurring challenges beyond data processing, such as data availability, integration, human involvement, operations, and business considerations. Addressing these challenges is crucial for progress in this domain, yet research support is lacking. We present these challenges, discuss our current solutions, and call on re...
Abstract: The automation of Unified Modeling Language (UML) sequence diagram generation has posed a persistent challenge in software engineering, with existing approaches relying heavily on manual processes. Recent advancements in natural language processing (NLP), particularly through large language models (LLMs), offer promising solutions for automating this task. This paper investigates the use of LLMs i...
Abstract: The global environmental crises continue to get worse, fast approaching various irreversible thresholds. While a vast array of approaches to solving sustainability problems are found under the umbrella of Sustainable HCI, their contributions are sometimes hard to compare. In this essay, we describe a set of assumptions that influence what is considered meaningful and important areas of sustainabil...
Abstract: Process debt, like technical debt, can be a source of short‐term benefits but often leads to harmful consequences in the long term for a software organization. Despite its impact, the phenomenon of process debt has not been thoroughly explored in current literature, leaving a gap in understanding how it affects and is managed within organizations. This paper addresses this gap by defining process ...
Abstract: Modern software-intensive systems are growing more complex, evolving continuously, and requiring extensive collaboration across diverse domains. Effective documentation and communication of architectural changes are critical to managing the development of such systems. But this task remains challenging due to constraints in time, resources, and standardized practices. This study investigates the p...
Abstract: When building a machine-learning-enabled system, quality objectives are achieved through architectural and non-architectural tactics, including general ones as well as specific ones that address machine learning specifics, such as the focus on data. However, implementing these tactics typically compromises other quality attributes that are not the primary focus of the tactic at hand. Previous rese...
Abstract: Internet of Things (IoT) devices generate large amounts of data, creating the challenge of designing efficient IoT cloud storage solutions. This study focuses on an IoT application managing air quality measurement data, which requires frequent retrieval of recent data for near-real-time monitoring and access to long segments of time-series data. The study performs a comparative analysis of three I...
Abstract: Regulatory standards for engineering safety-critical systems often demand both traceable requirements and specification-based testing, during development. Requirements are often written in natural language, yet for specification purposes, this may be supplemented by formal or semi-formal descriptions, to increase clarity. However, the choice of notation of the latter is often constrained by the de...
Abstract: Context and motivation: In recent years, behavior trees have gained growing interest within the robotics community as a specification and control switching mechanism for the different tasks that form a robotics mission. Problem: Given the rising complexity and prevalence of robotic systems, it is increasingly challenging and important for practitioners to design high-quality missions that meet cer...
Abstract: Model-based testing (MBT) generates test cases through a model representing the software under test (SUT). The generated abstract test cases need to be transformed into concrete or executable test scripts. Despite the benefits offered by MBT, its industrial adoption is slow. This paper aims to propose a Model-Based Test scrIpt GenEration fRamework (TIGER) based on GraphWalker (GW), an open-source ...
Abstract: Recent advancements in AI, especially generative AI (gAI), are accelerating industrial digitalisation, with the market projected to grow significantly by 2030. However, challenges such as the black-box nature of AI decisions, biased data, and AI-generated hallucinations continue to hinder industrial trust. AI also requires better adaptability to dynamic environments and stronger accountability mec...
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