To address the increasing complexity and diversity of user requirements in underwater rescue equipment, this study proposes a Digital Twin (DT)-enhanced KJ-Kano conceptual design framework. It systematically closes the feedback loop between requirement prioritization and experiential validation. Unlike traditional approaches, this framework orchestrates KJ clustering, Kano analysis, and mission-aware DT simulation in a domain-adapted, iterative workflow, enabling dynamic validation of user needs under high-risk, simulated rescue scenarios. Functional expectations and preferences were clustered and prioritized, then instantiated in a modular DT prototype for navigation, manipulation, and perception tasks. To evaluate design effectiveness, 55 participants operated the robot DT model and its control interfaces in virtual rescue missions. User satisfaction across functionality, interactivity, intelligence, and appearance was assessed with a five-point Likert scale, and the results showed high reliability (Cronbach’s α = 0.86) and positive evaluations (overall mean = 3.83). Intelligent experience scored highest (3.95), while ease of operation was lowest (3.60), suggesting potential for interface optimization. The framework effectively transforms heterogeneous, context-specific user requirements into validated design solutions, offering a replicable, data-driven methodology for early-stage conceptual design of underwater rescue robots and other safety-critical human–machine systems, bridging the gap between generic design methods and high-risk domain application.

Background: The integration of virtual reality (VR) and eye-tracking (ET) technologies has considerable potential for enhancing perceptual–cognitive skill development in basketball. However, existing research provides limited guidance on the technical and pedagogical requirements for designing VR–ET systems that are both instructionally meaningful and aligned with user needs. Aims: This study aims to identify these key design requirements through a user-centred mixed-methods approach. Methods: Semi-structured interviews and focus group discussions with coaches and lecturers were conducted to explore training challenges, expectations, and pedagogical considerations. Insights from the qualitative phase informed the development of a questionnaire administered to student-athletes (N = 120), allowing for the validation and prioritisation of system requirements. Qualitative data were analysed thematically, while quantitative data were examined using descriptive statistics. Result: The integrated findings reveal six essential domains for effective VR–ET system design: immersive tactical–technical visualisation, interactive decision-making scenarios, gaze-based performance indicators, real-time visual feedback, learning support features, and implementation readiness. Users emphasised the value of VR–ET for enhancing tactical understanding, attentional control, and personalised training experiences. Conclusion: This study contributes both theoretically and methodologically by addressing the lack of user-informed frameworks in VR–ET research and demonstrating the value of participatory mixed-methods approaches in sports technology development. The results provide a foundational design blueprint for future VR–ET prototypes and offer new insights into how immersive and gaze-based technologies can support athlete learning in team sports.

Software requirements prioritization: IDOCRIW and QUALIFLEX approach

User requirements prioritisation for research data management services (RDMS) in Myanmar university libraries: Multi-stakeholder perspectives using the KANO model

The prioritization of user requirements is neglected in most existing interface designs for driverless vehicle systems, which may incur safety risks, fragmented user experiences, development resource wastage, and weakened market competitiveness. Accordingly, this paper proposes a hybrid interface design method for a virtual reality (VR) driverless vehicle system by combining a A-KANO model and system usability scale (SUS). Firstly, we obtain key words, and a total of 23 demand points are collected through word frequency analysis via combining with user interview and observation method; secondly, 21 demand points are derived from A-KANO model analysis and prioritized for function development; and finally, design practice is carried out according to the ranking results, and virtual reality technology is used to build a VR unmanned vehicle system in order to simulate the interface interaction of a driverless vehicle system. Then, the VR driverless vehicle system is used as a test experimental environment for user evaluation, and combined with the SUS scale to evaluate the user-prioritized experience requirements for practical verification. Empirical results demonstrate that this method effectively categorizes multifaceted user needs, providing actionable solutions to enhance passenger experience and optimize service system design in future autonomous driving scenarios.

Requirements prioritization is crucial in software development, as it determines which requirements are addressed first to maximize project success. Previous studies have identified factors considered in requirements prioritization, but they have not fully captured the nuances and conditions present in the software industry. This study aims to investigate the key factors influencing requirements prioritization, with a particular focus on dependencies within the development model. Identifying these factors helps in understanding the complex interplay between various elements that ultimately impact project outcomes. This qualitative research combines a literature review with expert interviews to identify eight primary factors influencing requirements prioritization: cost, business value, urgency, human resources, risk, time, and dependencies. Five software industry experts, serving as project managers in the education, corporate, and government sectors, were interviewed. The interviews were conducted both online and offline, employing a semi-structured approach. The study explores how stakeholders and developers contribute to the prioritization process, with stakeholders emphasizing business needs and compliance, while developers focus on technical considerations. Our research findings highlight the key factors that influence requirements prioritization in the software industry. These factors require continuous evaluation and adjustment to ensure effective decision-making throughout the software development process. The proposed model integrates both stakeholder and developer perspectives, offering a structured approach to ensure alignment with organizational goals and optimize resource utilization. Notably, special attention is given to managing requirement dependencies, ensuring that interrelated tasks are effectively coordinated. This comprehensive model facilitates informed and strategic decision-making, thereby contributing to more effective software project management.

In software engineering, requirements prioritization is a critical stage that ensuresthe timely distribution of high-value structures while working with restricted resources. In this study, 19 popular requirements prioritization policies are associated, and their efficiency is studied from a range of approaches, counting scalability, accuracy, shareholder contribution, and related flexibility. The goal is to define which technique or techniques are best suitable for a particular project established on dynamics like team size, complexity of requirements, time, cost, and risk. This comparative study's main goal is to evaluate both modern techniques relating Artificial Intelligence, Fuzzy Logic, and Huge Language Models (LLMs) and more conservative methods such as Analytic Hierarchy Process (AHP), MoSCoW, Planning Game, and Value-Oriented Prioritization (VOP). The rewards of each method are examined concluded experiential case studies, which authenticate that LLM-based tools distribute automation and speed in Agile atmospheres, while AHP is dependable and dependable precisely. The procedure involved collecting and observing available research from 2015 to 2025 with an importance on relative presentation, shareholder satisfaction, and choice accuracy. Surveys, models, and relative testing were inspected to control the prizes and drawbacks of each process. The results show that hybrid mockups, such as restraint solvers, fuzzy AHP, and AI-assisted methods, perform better than outdated ones in relations of flexibility and usability, particularly in vibrant or important tasks. The revision proposes a context-aware method for operation, utilizing fusion/AI mockups where robotics and scalability are critical, MoSCoW for Agile groups, and AHP for systematic stability. In software development and condition manufacturing measures, this research supports in constructing well-read results.

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain.

ABSTRACTRequirements prioritization puts more emphasis on the software requirements based on their importance, making it a crucial activity in managing software requirements throughout the software development process. This work explains the concept of prioritization techniques, their relevance, and related issues. Recent literature has described many requirement prioritization techniques, but each comes with its own limitations and challenges, specifically regarding scalability. In this article, we provide a systematic literature review (SLR) of the requirement prioritization techniques over the past decade and identify their limitations and scalability issues. In this context, we develop a planned protocol that includes all the necessary steps required for the SLR process. As a result of conducting the SLR, 53 primary studies were shortlisted for data extraction. The analysis of the results indicates that there is a minimal focus on large‐scale functional requirements, with only 9% of the work addressing this area. To the best of our knowledge, no work has been done on prioritizing requirements between ERP systems and mobile‐specific applications. Most existing studies focus on imaginary or simulated projects, and very limited actual work has been carried out during the execution of the industrial projects. When requirements are prioritized correctly, the likelihood of successful implementation will be high, and it will lead to a higher quality product.

In today’s digital world, cybersecurity has become a critical factor in software implementation across various domains. The increasing complexity of systems, the dynamic threat landscape, and limited resources require software developers and stakeholders to adopt effective mechanisms for managing cybersecurity requirements. This article presents an integrated mathematical model for managing cybersecurity requirements during software implementation. The relevance of this problem is driven by the growing complexity of modern information systems, the need for compliance with international security standards, and resource constraints during project execution. The proposed model combines several mathematical techniques, including the Analytic Hierarchy Process (AHP), fuzzy logic, Bayesian networks, and mathematical programming, ensuring a systematic decision-making approach. The model developed by the authors enables prioritization of requirements, evaluation of compliance under uncertainty, risk modeling, and optimal resource allocation while considering budget constraints and security objectives. An experimental study based on the NIST SP 800-53 standard confirmed the effectiveness of the proposed approach in reducing risks without exceeding available resources. The results are practically significant for organizations implementing software solutions in alignment with modern cybersecurity requirements. Future research will focus on optimizing the model and applying it to other international standards.

The co-evolution of products and customers introduces increasing uncertainty about customer requirements (CRs), posing challenges for businesses striving to meet constantly changing CRs. Prioritising CRs is essential for resource allocation during product design. However, existing studies often ignore the dynamic natures (DNs) of these requirements, risking short-lived products. To bridge this gap, this study proposes a framework that constructs and integrates the DNs of CRs into the prioritisation process. First, an unsupervised Gibbs sampling Dirichlet multinomial mixture (GSDMM) extracts a thematic distribution from CRs, serving as a baseline for topic patterns. Building on this, a semi-supervised learning approach that leverages these distributions and embeddings from bidirectional encoder representations from transformers (BERT) improves the classification accuracy of user-generated content (UGC), enabling precise CRs extraction and sentiment scoring. The framework then constructs DNs, including sensitivity, variability, and randomness, by merging temporal data with sentiment analysis, while static natures are derived from CR frequency and sentiment. Finally, Choquet integral is applied to fuse the DNs and static importance, resulting in a more responsive CRs prioritisation strategy. A case study on a smart wristband validates the effectiveness of the method.

Abstract This paper introduces a novel prioritisation method, the Thematic Hierarchy Process, designed to work with software release cycles in a matrix product development organisation with software product lines and multiple business lines. Three challenges are identified for prioritisation of requirements for software product lines with multiple business lines: scale, complexity and stakeholder discordance. The paper reviews current prioritisation methods and assesses their application to software product lines with multiple business lines with respect to these three challenges, and concludes that none of the existing methods can satisfactorily address all of them. In this paper we define the Thematic Hierarchy Process method, and provide a framework that can be applied by software product managers and researchers to their own software releases and requirements data. The Thematic Hierarchy Process was assessed using requirements data from Company A demonstrating strong alignment with real software release cycles, as shown by the high similarity scores of 0.87 and 0.91 in predicting release contents. Interviews carried out with domain experts to evaluate the Thematic Hierarchy Process to supplement the assessments also showed positive results. We conclude that the Thematic Hierarchy Process can satisfy the prioritisation challenges of scale, complexity and stakeholder discordance with software product lines with multiple business lines.

Collaboration and sustainability-driven requirement prioritization for cloud platform planning oriented to value chain lifecycle services

Software startups operate under extreme uncertainty and financial pressure, making Requirements Prioritization (RP) a critical activity for improving survival prospects. This study investigates which prioritization criteria are most relevant to early-stage ventures by conducting a systematic literature review (SLR) of 40 studies and analyzing 358 RP references encompassing 82 distinct criteria across 10 categories. Additionally, we conducted 34 semi-structured interviews with founders from 19 software startups to compare academic insights with real-world practices. The analysis reveals a substantial gap between scholarly emphasis and practitioner needs: while "expected cost" is the most frequently cited criterion in the literature, criteria related to financial impact—such as return on investment, cash flow, and time to value—are underrepresented despite being consistently cited by the most successful startups in our sample. Notably, the criterion "Time to Value," absent from all reviewed literature, emerged as a key factor among practitioners. These findings highlight the need for a more financially grounded and context-sensitive approach to RP in startup environments. The study concludes with a call for greater alignment between academic frameworks and the realities faced by early-stage software ventures, especially in economically challenging conditions.

A Data-Driven Approach for Mining Truck User Requirements from Online Reviews

ABSTRACTFunctional requirements (FRs) prioritization is process of ranking of software FRs from development perspective such that which requirement to be implemented first and which should not. FRs prioritization is necessary as these requirements are interrelated such that one requirement is necessary for the implementation of another requirement. Also, when two parallel developers work on interrelated dependent requirements, requirements must be prioritized. Prioritizing small size requirements is not a big issue due to a fewer number of comparisons but when developers implement large size requirements such as enterprise resource planning (ERP), it requires a huge number of comparisons. Numerous techniques are suggested for FRs prioritization such as AHP, which yield more accurate results, but these techniques are not scalable for large size software requirements. In this research paper, a new prioritization approach based on graph and k‐means clustering is suggested that will capture all dependencies from a list of FRs using a directed graph and then prioritize it with a clustering technique with fewer comparisons. The proposed technique based on directed graph and clustering approach is validated on ODOO ERP, which shows that with n‐1 pairwise comparisons, requirements can be prioritized.

Requirement Engineering (RE) is a critical phase in software development, integral to the successful execution of projects. The initial stage of RE involves requirement elicitation and analysis, where the prioritization of requirements is critical. Traditional methods of requirement prioritization (RP) are diverse, each presenting unique challenges. In response to the challenges of traditional methods, this paper proposes an entirely automated framework designed to eliminate the disadvantages associated with excessive stakeholder involvement. This innovative framework processes raw natural language inputs directly, applying a three-phase approach to systematically assign priority numbers to each requirement. The first phase preprocesses the input to standardize and prepare the data, the second phase employs advanced machine learning algorithms to analyze and rank the requirements, and the third phase consolidates the results to produce a final prioritized list. The effectiveness of this method was tested using the RALIC (Replacement Access, Library, and ID Card) dataset, a well-known benchmark in the field of requirement engineering. The results confirm that our automated approach not only enhances the efficiency and objectivity of the prioritization process but also scales effectively across diverse and extensive sets of requirements. This framework represents a significant advancement in the field of software development, offering a robust alternative to traditional, subjective methods of requirement prioritization.

This paper aims to optimize the product design of nail tables and chairs and enhance user satisfaction. It proposes a comprehensive and visualized design process for developing dual-category user products that balance the needs of two distinct user groups: nail technicians and customers. Leveraging the Kano model, DEMATEL method, and TRIZ theory, the process includes four key steps: gathering dual-category user requirements, categorizing requirement attributes, analyzing the interrelationships between requirements, and resolving design conflicts. Using the design of nail tables and chairs as a case study, the paper empirically demonstrates how to balance the operational efficiency of nail technicians with the customer experience. This approach not only optimizes the design of nail tables and chairs but also offers valuable insights for requirement prioritization and iterative development of other dual-category user products.

Requirements Prioritization is a crucial part of Requirements Engineering which helps to prioritize the customer’s requirements according to his needs and priorities. This prioritization describes which requirements should be addressed first and which can be addressed later in the software development process. Researchers have suggested many methods and techniques of requirements prioritization. However, there is no comprehensive technique that can be used for all sizes of software projects. This research paper includes an overview of the concept of requirements prioritization, the most common techniques used to prioritize the requirements,and their comparison. Based on based on this comparison, a new requirements prioritization technique is presented in this paper which can be used for every size of a software project. This technique aims toprovide the solution tomany issues of previous techniques especiallydependencies of requirements, user involvement as well as designers involvement. The results demonstratedthat the RP modeloutperforms traditional techniques, particularly in agile development environments, by providing a more efficient and flexible prioritization process.The involvement of designers in requirements prioritization and handling of requirements dependencies reducesthe efforts required in the design process.

Millions of dollars and valuable resources are being wasted annually due to ineffective requirement prioritization methods in managing the complexities of Complex Adaptive Systems (CAS). Traditional prioritization approaches struggle significantly to adapt to the dynamic and evolving nature of Critical Asset Systems (CAS), especially within critical industries such as aerospace, defense, healthcare, and environmental management. These rigid methods often lead to project delays, budget overruns, and suboptimal performance, ultimately failing to meet stakeholder expectations. This research introduces a transformative approach by integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques into the prioritization process, aiming to dramatically enhance adaptability and responsiveness. Utilizing a mixed-methods strategy, this paper provides empirical validation through detailed mathematical modeling, quantitative assessments, and in-depth qualitative case studies. Notably, AI and ML-driven prioritization frameworks have shown marked improvements in real-time adaptability, operational efficiency, and resource allocation compared to traditional methods. Additionally, the paper systematically addresses critical challenges, including data integrity, algorithmic bias, and the necessity for interdisciplinary collaboration. By offering a robust analytical framework and demonstrating the effectiveness of AI-driven prioritization, this research provides valuable insights and practical guidelines for stakeholders and researchers. Ultimately, this work contributes significantly to the ongoing discourse on requirement prioritization, setting the stage for more resilient and efficient CAS management strategies in complex and dynamic operational environments.