requirements elicitation

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.

Prior work lacks a systematic approach to transform unstructured explanation requests from user feedback into structured explainability requirements and corresponding explanations. Method: This paper proposes the first end-to-end automated framework—rule-guided prompt engineering—that integrates large language models (LLMs) and NLP techniques to jointly identify explanation needs, generate structured explainability requirements, and produce natural-language explanations from user reviews, augmented by human validation for quality assurance. Contribution/Results: We construct and publicly release the first annotated dataset of 58 real-world user comments. Experiments show that AI-generated explanations often surpass manually authored ones in clarity and stylistic expressiveness; however, requirement correctness remains error-prone and necessitates human correction—highlighting the critical role of human-AI collaboration. This work establishes a novel paradigm and empirical foundation for explainable AI (XAI) in requirements engineering.

In requirements elicitation meetings, stakeholder dialogues contain rich requirement information, yet manual extraction is time-consuming, error-prone, and susceptible to subjective bias. Existing approaches primarily focus on dialogue summarization or requirement classification, lacking end-to-end capability to identify contextual cues and generate structured system requirements. This paper proposes a dual-granularity (utterance-level → meeting-level) requirement identification and generation framework that integrates natural language processing with large language models (LLMs), enabling fine-grained cue localization and executable requirement generation. Evaluated through combined performance analysis and engineer-led user studies, our method significantly outperforms baselines in correctness, completeness, and executability. It enhances elicitation efficiency while preserving human engineers’ authority in critical judgment tasks, thereby advancing the practical adoption of human-AI collaborative requirements engineering.

This work addresses the challenge of architectural knowledge fragmentation across heterogeneous software artifacts, which often leads to architectural degradation due to inconsistent evolution. The paper presents the first end-to-end automated framework for architectural knowledge management, featuring specialized extractors that harvest knowledge from diverse sources, a unified representation model, and integrated mechanisms for consistency checking and repair to preserve knowledge integrity. Innovatively leveraging Retrieval-Augmented Generation (RAG), the framework enables compliance verification, change impact analysis, and natural language question answering grounded in a structured knowledge base. An experimental prototype demonstrates the approach’s effectiveness in enhancing architectural understanding, maintenance efficiency, and intelligent interaction.

To address challenges in requirements engineering—including difficulty in identifying relationships among natural language requirements, high manual annotation costs, and poor domain adaptability—this paper proposes an NLP-driven, systematic relation extraction framework. It is the first to integrate a requirements relationship ontology with multi-paradigm NLP techniques: dependency parsing, semantic role labeling, named entity recognition, BERT-based supervised fine-tuning, and retrieval-augmented methods. A unified classification-based evaluation framework is established to clarify core challenges and evolutionary pathways. The framework supports major requirement relations (e.g., *refines*, *conflicts*) and enables reusable, extensible relation modeling. Experimental results demonstrate significant improvements in automation capability and accuracy for large-scale adaptive requirements management systems, thereby strengthening requirements evolution analysis and consistency verification.

This work addresses the inefficiency and insufficient accuracy inherent in extracting and classifying requirements from semi-structured documents within traditional requirements engineering. To overcome these limitations, the authors propose ReXCL, an end-to-end automated tool that integrates heuristic rules with predictive modeling for requirement extraction and employs an encoder-based deep learning architecture with adaptive fine-tuning to achieve high-precision classification. The output of ReXCL is designed for seamless integration into mainstream requirements engineering tools. Empirical evaluation in real-world requirements engineering scenarios demonstrates that the proposed approach significantly enhances both processing efficiency and classification accuracy, confirming its effectiveness and practical utility.

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 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.

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.

This study addresses the challenge of leveraging the rich testing knowledge embedded in Ethernet Switch Configuration Manuals (ESCMs), which remains underutilized due to their semi-structured format, implicit step attributes, and inter-chapter dependencies that hinder automated test generation. To overcome these limitations, this work proposes a multi-agent large language model (LLM) framework integrated with a fine-grained knowledge graph schema and an iterative Extract-Evaluate-Improve (EEI) mechanism. This approach achieves, for the first time, high-fidelity structured extraction of implicit configuration knowledge from complex industrial documentation. Evaluated on 50 real-world ESCMs, the method attains extraction accuracies of 0.97–0.99, with strong agreement between LLM-generated and human annotations (Cohen’s kappa ≥ 0.72). The resulting knowledge graph effectively supports the generation of correct and practical test case specifications.