technical product translation

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.

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.

Existing RTI policy monitoring tools suffer from inefficient information acquisition and inadequate dynamic tracking capabilities. This study proposes a methodology for developing an open-source web-based system dedicated to Research, Technology, and Innovation (RTI) policy monitoring. It employs role-driven requirements engineering to precisely elicit heterogeneous stakeholder needs and introduces a novel modular architectural paradigm centered on a user-configurable dashboard, with strict separation across presentation, service, and data layers. The approach integrates open-data interoperability standards and interactive visualization techniques. As key contributions, the work delivers a reusable RTI monitoring system architecture specification and a standardized dashboard requirements template. These artifacts were empirically validated through deployment in the Austrian RTI Monitor—a national-scale platform enabling cross-departmental, real-time indicator tracking and evidence-informed policy coordination—thereby substantially enhancing the timeliness, accessibility, and scalability of RTI policy monitoring.

In IT consulting, requirements specification writing faces challenges including fragmented domain knowledge and excessive time consumption. This paper proposes a human–AI collaborative requirements engineering paradigm: leveraging large language models (LLMs) as draft-generation engines, integrated with requirements summarization, template-guided structuring, and prompt engineering to automatically generate Epic-level Functional Design Specifications (FDS) and user stories. Human analysts focus on contextual understanding and technical validation, ensuring semantic accuracy and engineering feasibility. Experiments demonstrate that the approach reduces documentation time by 2.3× on average and cuts human effort by ~40%. Generated FDS documents achieve near-human performance in structural completeness and readability, with >92% coverage of critical requirements and manageable revision overhead. The core contribution is the first LLM-augmented requirements documentation framework tailored to consulting contexts—balancing automation efficiency with engineering reliability.

This study addresses the lack of a unified theoretical foundation in traditional requirements engineering (RE) quality assessment, which often fails to integrate artifact- and process-oriented perspectives and overlooks information transmission efficiency. To bridge this gap, the paper proposes a holistic theoretical framework that models RE as a flow of information particles among stakeholders, developers, testers, and artifacts, with information flow as its core construct. Building on this model, the authors develop a simulation system to capture dynamic interactions and information exchanges across roles. The simulation reveals how high-quality requirements specifications can be inadvertently bypassed in agile environments and yields actionable insights for improving RE processes. This work establishes a theoretical basis for optimizing information flow, enhancing RE effectiveness, and understanding the underlying causes of success or failure in requirements engineering practices.

This work addresses the error-prone and labor-intensive process of manually translating regulatory texts such as the GDPR and the EU AI Act into actionable software requirements. The authors propose Reg2Req, the first end-to-end automated pipeline that leverages natural language processing to identify regulatory provisions, generate system-agnostic software requirements accompanied by plain-language explanations, and establish traceability links. The approach supports requirement classification, use case seed generation, and cross-reference analysis, achieving macro-averaged F1 scores of 0.82 on the GDPR and 0.78 on the EU AI Act. A user study demonstrates that the generated plain-language explanations significantly enhance users’ comprehension and confidence in taking compliance actions (p < 0.001), with all participants expressing willingness to adopt the output as a starting point for compliance efforts.

Software requirements are often implicit in stakeholder interviews, making them difficult to capture explicitly yet critically important for system design. This work proposes LENS, a novel approach that leverages context-aware large language models (LLMs) to jointly extract explicit requirements and infer implicit ones from interview transcripts, while incorporating organizational context to generate traceable user stories. LENS enables unified modeling and traceability of both explicit and implicit requirements. Evaluated on 12 interview transcripts from the cybersecurity domain, the method achieves an F1 score of 84.4% in explicit requirement extraction, and 75% of the inferred implicit requirements were rated by domain experts as practically valuable, demonstrating its potential to support automation and reduce manual analysis effort.