constraint elicitation

This study addresses the challenges posed by the proliferation, complexity, and expanding scope of regulatory requirements in software engineering, which hinder their systematic integration into development processes. To tackle this issue, the paper proposes a viewpoint-centered, artifact-based approach to regulatory requirements engineering. The approach innovatively integrates viewpoint analysis with artifact modeling to develop the AM4RRE (Artifact Modeling for Regulatory Requirements Engineering) framework, which facilitates cross-functional collaboration and ensures consistency in compliance-driven design. Preliminary validation demonstrates that AM4RRE effectively bridges the gap between organizational regulatory processes and software development practices, enabling a shift from ad hoc compliance responses toward systematic integration. This foundational work paves the way for further empirical investigation into scalable and sustainable regulatory compliance in software engineering.

In safety-critical, regulated domains (e.g., aviation, healthcare), AI-driven design artifact generation tools suffer from poor explainability, leading to non-auditable decisions, high manual verification overhead, fragmented cross-role collaboration, and regulatory non-compliance risks. To address this, we conducted semi-structured interviews with domain experts and performed qualitative analysis to systematically identify explainability bottlenecks hindering the integration of NLP tools into requirements engineering. Building on these insights, we propose the first compliance-oriented explainability framework for such contexts, comprising four interlocking mechanisms: (1) decision provenance tracing, (2) explicit justification of generated artifacts, (3) domain-knowledge-aware adaptation support, and (4) automated regulatory compliance validation. Empirical evaluation demonstrates that the framework significantly enhances output transparency and trustworthiness, reduces manual review effort, strengthens stakeholder confidence, and improves collaborative efficiency—thereby offering both a practical implementation pathway and theoretical foundation for responsible AI deployment in high-assurance settings.

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.

Emerging, context-dependent trustworthiness requirements in AI systems lack structured specification methods. Method: This paper proposes the first integrated trustworthy AI requirements framework unifying AMDiRE (Artifact-based Requirements Engineering) and PerSpecML (multi-perspective specification for ML systems), enabling systematic modeling, traceability, and verification of trustworthiness requirements for non-deterministic AI operating in dynamic environments. Contribution/Results: It introduces the first deep integration of artifact-based requirements engineering with multi-perspective ML specification, establishing an end-to-end mapping from abstract stakeholder trust concerns to executable, formal requirements specifications. A prototype implementation demonstrates feasibility and identifies key open challenges—scalability, cross-perspective consistency, and runtime adaptability—thereby providing a systematic foundation for trustworthy AI requirements engineering practice.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This work addresses the semantic gap between tactical Domain-Driven Design (DDD) patterns and general-purpose modeling languages, which often leads to persistent misalignment between design intent and code implementation. To bridge this gap, the authors propose a DDD-native metamodel that treats tactical DDD constructs as first-class modeling primitives and embeds expert architectural knowledge as executable constraints. Integrated with a real-time constraint validation engine and a bidirectional round-trip engineering mechanism, the approach ensures continuous consistency between models and code. By doing so, it substantially lowers the barrier to adopting tactical DDD, transforming it from an expert-dependent, elite practice into a tool-supported, widely reusable engineering methodology.

This work addresses the inefficiencies inherent in manual construction of reference and influence models within Design Research Methodology (DRM), including poor readability, difficulty in modification, and challenges in tracing evidential support. To overcome these limitations, the paper introduces DREAMS, a novel modeling environment that, for the first time, articulates DRM-specific modeling support requirements. DREAMS incorporates typed causal modeling and symbolic relationship representation, directly anchoring hypotheses, empirical inputs, and literature citations to causal links. It further integrates interactive layout optimization and efficient retrieval mechanisms. Preliminary user evaluations demonstrate that DREAMS significantly reduces model creation and revision time, minimizes node reordering and edge crossings, and enhances both evidential traceability and overall model maintainability.

This work addresses the lack of systematicity in engineering system design, often caused by ambiguous requirements and poor traceability, as well as the prevailing focus of existing AI tools on solution generation rather than problem formulation. To bridge this gap, we propose Design-OS—a lightweight, specification-driven five-stage design process that ensures end-to-end traceability from conceptual to parametric representations through structured design artifacts. For the first time, we extend specification-driven human-AI collaboration from software to physical system design, integrating control theory with systems engineering principles. The framework enables human-AI co-execution via autonomous agents within a unified, auditable, and hardware-agnostic workflow. We demonstrate its generality and reproducibility on two rotary inverted pendulum platforms, with open-sourced templates and complete design artifacts significantly enhancing transparency and systematic rigor.

This work addresses the creative stagnation often induced by existing generative design tools that directly output complete images. To overcome this limitation, the authors propose a multi-stage, compositional AI-assisted design approach that emulates professional designers’ workflows: it first structurally interprets ambiguous design requests, then generates candidate elements—such as objects, backgrounds, typography, layout, and composition—separately, and finally enables interactive recombination. This method formalizes real-world design processes into a computable system for the first time, decoupling requirement interpretation, element generation, and composition to substantially enhance prompt diversity and alignment with user intent. User studies demonstrate that the system outperforms baseline approaches in both requirement comprehension accuracy and designer-rated quality, revealing a productive trade-off between structured workflow, creative clarity, and efficiency despite slightly longer generation times.