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Abstract: A common approach for producing graphical views from models is to transform them into textual representations that can then be rendered as diagrams using off-the-shelf diagram-as-code tools such as Mermaid, PlantUML, and Graphviz. Although this approach offers a practical and cost-effective solution for model visualisation, fine-grained traceability from generated views back to their corresponding...
Abstract: Blended modeling organizes one or more concrete syntaxes over a shared abstract syntax; under this inclusive definition, conventional single-syntax modeling is a special case. The distinctive multi-syntax case additionally requires cross-notation synchronization, impact analysis, and coherent review. We propose LLM-supported blended co-modeling: a semantic-delta protocol in which LLMs submit versi...
Abstract: Software quality assurance is pivotal in safety-critical domains such as railway systems, where failures could have catastrophic consequences. In this context, the train control and management system, which enables communication and control across multiple subsystems (such as doors and information panels) within a modern train, and its software must undergo rigorous validation. Alstom Rail Sweden ...
Abstract: Research on equation-based cyber-physical systems modeling languages, such as Modelica, is constrained by the lack of curated benchmark datasets. This limits empirical insight into the evolution and development of models. We address this gap with ModBench, a pipeline that mines Git repositories of Modelica libraries to produce benchmark datasets of model snapshots. The pipeline (1) filters reposit...
Abstract: Context: Data annotation is a foundational software engineering activity in the development of AI-enabled perception systems (AIePS) for safety-critical automotive domains. Despite quality assurance, recurring data annotation errors (DAEs) still degrade robustness, safety, and trustworthiness. Prior work often treats DAEs as execution-level defects, leaving their upstream, requirements-level origi...
Abstract: AI-based test agents promise to accelerate software testing by shortening feedback loops in continuous development and improving scalability and maintainability. To realize these benefits, engineers must still be able to assess if agent outputs are useful, valid, and reliable, rather than treating them as credible because they come from a capable system. This paper argues that overreliance on AI i...
Abstract: AI-based coding assistants are transforming software development by shifting effort from writing code to crafting prompts that guide code generation. Despite their growing importance, little empirical evidence exists on how prompts function as software engineering artifacts. Building on prior work framing prompts as mixed artifacts combining requirement intent and solution guidance, we build on th...
Abstract: This paper explores how co-opetition shapes interactions in System of Systems in which autonomous constituent systems must simultaneously collaborate and compete. While co-opetition is well established in business literature, its manifestations within System of Systems differ due to the operational independence, voluntary participation and heterogeneous objectives of constituent systems. Drawing o...
Abstract: The impact of applying generative AI tools to requirements engineering (RE) in industrial practice remains poorly understood. This paper examines how AI-assisted RE tools are used in industrial practice at XITASO, a medium-sized enterprise for high-tech software engineering, and how they reshape workflows, tool integration, and PO--developer relationships. We combine a 2024 company-wide use-case s...
Abstract: System of Systems architectures are increasingly used in complex domains such as multimodal transportation, where the secure onboarding of new constituent systems is essential for maintaining trust, interoperability and resilience. While onboarding traditionally emphasises functional integration, its cybersecurity implications remain insufficiently understood, particularly in Directed and Acknowle...
Abstract: Large Language Models (LLMs) are increasingly applied to software engineering (SE), yet their potential for autonomous, role-oriented collaboration remains largely underexplored. Understanding how multiple LLM-based agents coordinate, maintain role alignment, and converge on solutions is critical for SE, as naively allowing agents to interact does not reliably lead to correct or stable outcomes. R...
Abstract: This study presents an integrated framework combining scientometric analysis, machine learning (ML), and explainable artificial intelligence (XAI) to predict the unconfined compressive strength (UCS) of lime and cement-stabilised soils. A century-spanning scientometric review of lime- and cement-based soil stabilisation literature (1912–2026), complemented by a focused scientometric assessment of ...
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