product requirements definition

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.

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.

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.

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 study addresses core conceptual challenges in requirements engineering (RE) concerning legal requirements (LRs)—including definitional ambiguity, inconsistent conceptualization, ill-defined attributes, and weak empirical grounding. Adopting a rapid literature review methodology, we systematically coded and analyzed how LRs are defined, classified, assigned functional or non-functional status, and characterized with respect to dynamism, overlap, and implementability across RE literature. Our analysis reveals, for the first time, that LRs are routinely reduced to static compliance baselines; suffer from definitional inconsistency, insufficient operationalization, and limited empirical validation; and lack consensus on theoretical positioning. We thus propose reconceptualizing LRs as a distinct requirement type characterized by normative bindingness, dynamic evolution, and cross-domain dependency. This work establishes a rigorous conceptual foundation and empirical basis for modeling, verifying, and governing LRs in RE practice.

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 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.

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 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.

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.