requirements refinement analysis

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.

Existing research lacks systematic methods to assess how requirements engineering (RE) impacts downstream development activities, hindering RE process optimization. Method: This paper proposes the first fitness-for-purpose RE impact assessment model, integrating a systematic literature review with multi-source empirical data to identify and structure 24 downstream development activities affected by requirements and 16 quantifiable attributes. Contribution/Results: The model bridges two critical gaps in requirements quality assessment—namely, the “activity dimension” and “measurability of impact”—by enabling empirical analysis of how specific requirements artifacts and processes concretely influence development practices. It provides a theoretically grounded framework and evidence-based decision support for precise, targeted optimization of the RE phase.

Existing research lacks a systematic integration of how domain knowledge is effectively elicited, formalized, and sustainably maintained in Requirements Engineering (RE). To address this gap, we employed a hybrid retrieval and iterative snowballing approach to systematically analyze 75 primary studies, thereby establishing— for the first time—the foundational Knowledge-Driven Requirements Engineering (KDRE) framework. Our analysis reveals domain knowledge’s pivotal role in understanding system context, reconciling stakeholder concerns, and resolving requirement ambiguity. We further map its application patterns and challenges across mainstream requirement types (e.g., functional, non-functional), critical quality attributes (e.g., scalability, security), and knowledge management practices (e.g., traceability, evolution). The study identifies three key research directions: scalable knowledge representation, automated knowledge integration, and sustainable tool-chain embedding. This work provides RE researchers and practitioners with a rigorous methodological foundation and a clear roadmap for future investigation and practice.

This study addresses the challenge that stakeholders often struggle to articulate their true requirements accurately due to limited domain knowledge or cognitive biases, leading to misalignment between stated needs and underlying intentions. To bridge this gap, the authors propose a user-centered approach that leverages large language models (LLMs) to contextually rewrite initial requirements, followed by an iterative human-in-the-loop feedback mechanism for validation and refinement. As the first empirical investigation of its kind, the work demonstrates the efficacy of LLMs as assistive tools in requirements elicitation. In an evaluation involving 130 requirements from 26 participants, LLM-rewritten versions significantly outperformed original statements in intent alignment, readability, logical coherence, and unambiguity, while also uncovering latent requirement details, thereby enhancing both the accuracy and completeness of the requirements gathering process.

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

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.

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.

This study addresses the prevalent ambiguity, inconsistency, and incompleteness in articulating explainability requirements for AI systems due to a lack of standardized specifications. Through a structured literature review and interviews with developers, the authors identify a set of explainability quality attributes, which are then refined via a large-scale survey of practitioners into ten core attributes. For the first time, these attributes are translated into a prioritized, actionable guideline for writing explainability requirements. Building on this foundation, the authors design a lightweight, iterative requirements engineering workflow augmented by a large language model to assist in requirement generation. An accompanying web-based tool reduces average requirement drafting time by 23.5%, and user evaluations indicate that the generated requirements match or slightly exceed manually written ones in terms of implementability and textual quality.