nonfunctional requirements specification

Non-functional requirements (NFRs) are frequently missing or difficult to identify early in software engineering, particularly from functional requirements (FRs). Method: This paper proposes the first quality-attribute-driven, large language model (LLM)-assisted NFR generation framework. It integrates customized prompt engineering with a Deno pipeline and strictly aligns with the ISO/IEC 25010:2023 standard. The framework supports collaborative NFR generation across eight state-of-the-art LLMs (e.g., Gemini-1.5-Pro, Llama-3.3-70B). Contribution/Results: We conduct the first multi-LLM comparative evaluation, generating 1,593 NFRs from 34 FRs. Expert assessment yields average scores of 4.63/5.0 for NFR validity and 4.59/5.0 for attribute appropriateness, with 80.4% accuracy in quality-attribute classification—demonstrating the feasibility and practicality of automating high-quality NFR derivation in 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.

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.

Software maintainability is frequently overlooked in requirements engineering, often addressed only implicitly through informal specifications or tool-based suggestions, without explicit goals or proactive management. This paper proposes a systematic framework for defining explicit maintainability requirements goals. It introduces the first adaptation of the QUPER model to the maintainability domain, integrating quantitative maintainability measurement tools and industry benchmarks to enable organizations to specify measurable, traceable, and actionable goals. The framework is developed and empirically validated using design science research methodology, with industrial case studies confirming its effectiveness in elevating maintainability’s priority within development decision-making. Key contributions include: (1) establishing maintainability as an explicit, goal-oriented requirement engineering concern; (2) providing the first QUPER-based approach for modeling and calibrating maintainability goals; and (3) delivering a practical, process-integrated solution deployable within real-world requirements engineering workflows.

In software requirements engineering, manual translation of high-level abstract features into testable functional requirements (FRs) suffers from low efficiency and poor interpretability. To address this, we propose EasyFR—a framework that formalizes FR generation as a structured slot-filling task guided by Semantic Role Labeling (SRL). Our key contributions are: (1) a novel configurable dual-variable SRL template system; (2) the Key2Temp model, which automatically maps features to optimal template variants; and (3) the first integration of an SRL-guided mechanism into the decoding process of pretrained language models. Experiments across four public benchmarks demonstrate that EasyFR significantly outperforms state-of-the-art NLG models, including GPT-4. Ablation studies confirm that SRL template recommendation critically enhances generation quality. This work establishes a new paradigm for automated, reusable, and interpretable FR synthesis.

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 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 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 work addresses the challenge of reliably conveying intent, requirements, and constraints in human–AI–tool collaborative software development by proposing a specification-centric Bosque API (BAPI) ecosystem. The system introduces a highly expressive specification language that, for the first time, enables cross-language interoperability, automated test generation, formal verification, and execution sandboxing across the entire API lifecycle—from requirement definition and implementation to invocation and validation. By providing end-to-end specification guarantees, BAPI significantly enhances system correctness, security, and the efficiency of human–AI collaboration, offering a novel infrastructure for software development in the era of AI agents.

This work addresses the challenge of ensuring trustworthiness and stakeholder alignment in machine learning system development, which is often hindered by the absence of systematic requirements engineering. To bridge this gap, the authors propose REAL, a novel framework that uniquely integrates failure mode analysis into the requirements engineering process. REAL establishes a tripartite principle centered on data, model, and holistic system requirements, enabling iterative and traceable requirement refinement. Through a model-driven, stakeholder-oriented design, REAL demonstrates substantial improvements in requirement satisfaction in an autonomous driving case study. The authors further support reproducibility by releasing an open-source implementation toolkit.