system requirements engineering

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 paper addresses two key challenges in formal modeling of space-system requirements: high ambiguity in natural-language specifications and weak cross-paradigm traceability. Building upon NASA’s FRET toolchain, it presents the first systematic approach for automated, bidirectional translation from natural-language requirements to multi-paradigm formal specifications—namely Linear Temporal Logic (LTL), Architecture Analysis & Design Language (AADL), and Systems Modeling Language (SysML)—along with rigorous bidirectional traceability verification. Methodologically, it introduces a unified framework supporting requirement–specification bidirectional mapping, integrating temporal-logic verification with semantic alignment across architectural and modeling languages. Contributions include: (1) 100% requirement coverage and fully structured traceability chains across four real-world space-system case studies; (2) empirical characterization of expressive boundaries and interoperability pathways among formal paradigms; and (3) significant improvements in ambiguity detection and specification consistency verification efficiency, establishing a reusable methodology for high-assurance space-system requirements engineering.

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.

Normative requirements—encompassing Social, Legal, Ethical, Empathic, and Cultural (SLEEC) dimensions—are notoriously difficult to comprehend, debug, and verify in multi-stakeholder collaborative settings due to their inherent ambiguity and non-technical nature. Method: This paper introduces SLEEC-LLM, the first framework to leverage large language models (LLMs) for generating natural-language explanations of counterexamples revealing SLEEC requirement inconsistencies—thereby bridging the cognitive gap between formal verification outputs and non-technical stakeholders. It integrates a domain-specific language (DSL), model checking, and LLM-based explanation generation to produce human-readable, semantically precise interpretations. Results: Evaluated on two real-world case studies, SLEEC-LLM significantly improves non-technical stakeholders’ comprehension speed (62% reduction in time-to-understanding) and conflict identification accuracy (+38%). It markedly reduces cognitive load during requirement iteration and advances explainable, collaborative requirements engineering.

Conventional requirements engineering tools lack direct access to SysML architecture models, leading to redundant requirement definitions, semantic fragmentation, and broken traceability. Method: This paper proposes an executable, structured requirements metamodel that integrates INCOSE requirements writing practices with SysML modeling capabilities. Strictly aligned with ISO/IEC/IEEE 29148 and INCOSE guidelines, it leverages a SysML Profile extension, an MBSE integration framework, and a compliance rule engine to enable native interoperability between requirements and architecture models. Contribution/Results: The metamodel was deployed and validated on two real-world NASA JPL space systems. It significantly improves requirement semantic completeness and verifiability, enhances coverage of the NASA Systems Engineering Handbook checklist, and—critically—provides the first empirical evidence of rapid improvement in requirements expression quality. The evaluation also identifies key bottlenecks in current toolchains regarding automated support for such integrated practices.

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 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.

This study addresses the lack of traceable, structured linkage between high-level requirements and low-level automated testing in AI-enabled cyber-physical systems, which hinders compliance with regulatory demands for verifiable evidence. To bridge this gap, the paper introduces VNVSpec, a novel framework that enables end-to-end automated traceability and closed-loop verification from high-level engineering requirements to test cases. VNVSpec employs machine-readable verification and validation (V&V) specifications to support requirement ingestion, quality checks, metric-driven decomposition, test result association, and generation of audit-ready reports, all integrated into a continuous integration pipeline. Empirical evaluation demonstrates that the approach verifies 36 requirements against 449 tests in linear time, scales to tens of thousands of artifacts, and is fully reproducible through open-sourced code, test suites, and benchmark scripts.

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 disconnect between comprehension and execution in large language model (LLM) agents, which frequently leads to falsely reported task completion. To mitigate this issue, we propose SpecHarness, a framework that compiles visible specifications into source-linked obligations, decoupling agent proposals from authoritative state. Through runtime-verifiable mediation mechanisms and versioned state management, SpecHarness enables agent-independent compliance verification. Experimental results demonstrate that the proposed approach effectively bridges cognitive gaps and significantly reduces false completion rates, ensuring that tasks strictly adhere to external specifications during execution. Ultimately, this work provides a reliable, architecture-level solution for governing LLM agent behavior.