BackgroundA large amount of data are generated in health care facilities, yet it is rarely made available for secondary research use. The reasons are manifold. Most importantly, different stakeholders’ needs must be balanced. However, there are currently hardly any feasible solutions for this.ObjectiveThis study aimed to develop a data trust model with supporting user interface applications to provide a legally and ethically sound framework for secondary use of medical data. The development was based on extensive surveys of various stakeholders.MethodsSemistructured interviews were conducted with researchers (data users) and institutional representatives of the Medical Center–University of Freiburg, and online questionnaires were administered to patients (data subjects), data users, and institutional representatives. The questionnaire for data subjects covered the dimensions of trust (measured with a 5-point Likert scale), quality of interaction and involvement (measured with a 4-point Likert scale), subjective and objective understanding, and usability (measured with the user version of the Mobile Application Rating Scale). For all other stakeholder groups, the questionnaire focused on usability measured using the user version of the Mobile Application Rating Scale. The surveys comprised a requirement elicitation followed by two rounds of evaluation. Independent-samples Welch t tests were used to compare group means between the first and second evaluations.ResultsWe devised SouveMed, a framework for secondary use of medical data, applied to the use case of sleep research data. The model includes secure onboarding of data subjects and using digital consent and a digital interface for data users for onboarding, defining research aims, querying the amount of available data, and, finally, either downloading data or having algorithms run on it. At its core is a data trust entity that matches descriptions, consents, and constraints of all stakeholders using digital representations and constraint-solving techniques. Fourteen participants took part in the requirements elicitation, 22 in the first evaluation, and 16 in the second evaluation. In both the first and the second evaluations, data subjects showed a high level of trust in the concept, with mean ratings on the trust scale of 4.23 (SD 0.46) in the first and 4.23 (SD 0.68) in the second evaluation (t15.78=0.03, P=.97). Regarding usability, the mean functionality score of the data user system increased from 3.56 (SD 0.77) in the first to 4.58 (SD 0.38) out of 5 points in the second evaluation (t10.69=−3.28, P=.008). The mean functionality score of the data subject system increased from 4.30 (SD 0.41) in the first to 4.50 (SD 0.74) in the second evaluation (t13.99=−0.75, P=.46).ConclusionsThe SouveMed concept provides a comprehensive framework for the secondary use of medical data. The developed processes can be adapted to other areas of medical research.

Consensus-based elicitation and analysis of product sustainability requirements for effective strategy formulation in product design

Quality issues in User Stories (US) frequently arise during implementation, with common problems including ambiguity, integration challenges, and inconsistencies in templates or formal structure. These challenges emphasize the critical need to evaluate the quality of the US, prompting extensive research focused on developing methods and innovations to improve the requirement elicitation process. This study aims to analyze the frameworks, application methods, and tools employed in previous research to assess US quality. Utilizing the Kitchenham framework, 26 relevant studies were systematically reviewed. Next, the analysis identified six prominent quality evaluation frameworks: INVEST, Quality User Story (QUS), ISO 25010, IEEE 830, USQM, and linguistic-based approaches. The application of these frameworks often involves experts, practitioners, and artificial intelligence (AI). Meanwhile, among the tools reviewed, AQUSA emerged as the most frequently used due to its alignment with QUS standards. These findings highlight the adaptability of existing frameworks and tools while underscoring the potential for further integrating generative AI to enhance the accuracy and efficiency of US quality evaluations. Subsequently, future research should explore innovative AI-based methods to advance this critical area of requirement engineering.

Smart product-service system (SPSS), as an outcome of the integrated development of information technologies and digital services, has become an essential outcome of the cyber-physical system (CPS) innovation. To support SPSS innovation design, it is meaningful to conduct requirement engineering with high efficiency and accuracy. However, the complex characteristics of SPSS and CPS present challenges in effectively utilising user-generated content, achieving accurate requirement analysis, and characterising the coupling relationship between product and service requirements. Existing approaches tend to be static and fail to address the dynamic nature of requirements for SPSS. To meet the challenges, this study proposes an integrated requirement elicitation, sentiment analysis, classification, importance calculation and mixed requirement evolution (ESCI-MR) method, which combines advanced deep learning algorithms and classic design theories. By eliciting requirements from online reviews on e-commerce platforms, natural language processing algorithms are employed to identify requirement keywords and sentiment information from extensive datasets. These keywords are then clustered using a clustering algorithm, and sentiment analysis is performed to enable requirement classification and importance computation. Following this, the method delineates the evolution of requirements by constructing multidimensional indicators. Applied to the sweeping robot system, the method's efficacy and advancements are validated, thereby bolstering the innovation process.

Developing the necessary skills in Software Engineering students to conduct effective requirements elicitation interviews is a complex challenge. Immersive role playing has emerged as a promising educational strategy, enabling students to simulate realistic interviews, receive real-time feedback, and improve their performance under pressure. This approach blends traditional role playing with immersive learning environments, providing engaging and authentic experiences that better prepare students for industry demands. This article presents an experience report on the use of an immersive role playing to teach the interview technique for requirements elicitation. Conducted with 86 undergraduate students enrolled in a Requirements Engineering course, the study offers a broader perspective on the effectiveness and challenges of this approach. The findings suggest that immersive activities foster reflection, help identify areas for improvement, and emphasize the importance of emotional regulation in real-world interactions. These insights reinforce that mastering Requirements Engineering requires not only technical proficiency, but also strong interpersonal and emotional skills.

Requirement Elicitation plays a great role in the successful completion of a software project. Elicitation is a productive effort to extricate project-associated information from the stakeholders. To gather complete, concise, and clear requirements, the concept of requirement elicitation allows various analytics and techniques. Requirement elicitation is significant due to the lack of efficiency in accurately articulating their demands towards the intended system. Therefore, requirement engineers realize the need to perform elicitation to ensure that the requirements produced are easily accessible, useable, and applicable according to the client’s demands. A software project can be considered successful if it satisfies all the requirements of the client. In certain situations, changes in requirements from the client side may arise during the development of the intended software. This research focuses on the challenges of this context mentioned as follows-many stakeholders are unable to clearly express their needs toward the intended system and the client-side requirements may change often. In this case, traceability plays a vital role in making the necessary changes in the components of the corresponding requirements. Therefore, the impact of any change can be easily visible & the changes can be applied correspondingly. After all, a project’s success can be achieved by ensuring traceability among the components used in eliciting requirements. So, this research proposes an effective approach for eliciting functional and non-functional requirements and ensuring traceability among the corresponding components used in eliciting requirements. In addition, a tool has been designed to ensure traceability among functional requirements, non-functional requirements, and other corresponding components. If any change request arises to any of the requirements, all the corresponding components associated with that requirement can be traced with the help of this tool. In overall, this proposed approach has been applied successfully to a case study of an Automated Teller Machine using this tool.

Automatic requirements elicitation from user-generated content: A review of data, methods, and representations

Requirements elicitation is one of the most important steps in the software development process. It involves understanding what users and stakeholders need from a system before it is built. Traditionally, this has been done using methods like interviews, questionnaires, document reviews, and direct observation. These approaches work well in structured environments but often fall short when dealing with large, fast-changing, or agile projects. In recent years, software development has shifted toward more flexible and fast-paced practices. This change has also affected how requirements are gathered. New techniques now include collaborative tools, user feedback from online platforms, and the use of artificial intelligence (AI) and natural language processing (NLP) to extract requirements from text automatically. This paper presents a comparative narrative review based on recent literature and practical insights. It presents both traditional and modern requirements elicitation methods, comparing them in terms of how they work, where they are most useful, and what challenges they present. A detailed comparison highlights key differences such as level of user interaction, tool support, scalability, and suitability for ongoing development cycles. By reviewing recent research and real-world practices, this paper identifies current trends, challenges, and open areas for future work. The goal is to help researchers, software engineers, and project teams choose the most suitable elicitation methods based on their specific project needs. In the end, this review supports the idea that a flexible, hybrid approach-blending old and new techniques-may be the most effective way forward in today's evolving software engineering landscape.

Aircraft design and development is complex and regulated by increasingly stringent regulatory documentation. While many disciplines manage design complexity with well-established digital tools, digital transformation of the certification process remains in the early stages of implementation. Models are often used to provide explicit structures to facilitate digital transformation. While several modeling approaches have been applied to regulatory documentation, a gap remains for an established list of requirements for developing effective models in the context of digital transformation. This paper proposes a list of requirements using a requirements elicitation framework adapted from the International Council on Systems Engineering (INCOSE) Needs and Requirements Manual. The adapted research methodology includes problem identification, needs assessment, and requirements development processes. The resulting requirements are validated against needs statements and verified against selected INCOSE requirement statement criteria. Four requirements are selected for a detailed feasibility assessment, which compares the efficacy of process mapping, Unified Modeling Language (UML), and ontological modeling methods to realize the requirements.

Since several years, sustainability has become a very important challenge for our societies. Our lifestyles are in the process of making our planet uninhabitable because of the various impacts that we, as human beings, are inflicting on it. As part of these impacts, we focus on Complex Systems designed by humans. It is of uttermost importance to be able to analyze and design complex systems so that the sustainability features are taken into account. More specifically, the contribution of this article is to propose models and tools for the assessment of systems sustainability during the analysis and design phases. The analysis and design of systems may be difficult tasks. It is even more so for complex systems. Since decades, the System Engineering (SE) field has given birth to a family of systemic and multidisciplinary approaches for the design of systems. Among SE approaches, Model-Based System Engineering (MBSE) is a special kind of SE that relies on formalized models as first-class citizens deliverables for all analysis and design activities from requirements elicitation to final design and validation. SysML (Systems Modelling Language) is one of these MBSE approaches. SysML is a well-known, general purpose graphical systems modelling language that supports SE approaches. In addition, SysML allows its own language extension by the creation of new concepts and diagrams. This extension mechanism is known as Domain Specific Modelling Language (DSML). The contributions presented in this paper consist in the definition of an extension of SysML that provides models and tools in order to assess the sustainability of systems during analysis and design. This extension of SysML is based upon a technique, named profile, and proposes new modelling concepts for taking into consideration sustainability issues during systems engineering. Moreover, these new model elements are supported by software tools issued from the MBSE domain and allow the development of ad-hoc software tooling support.

This article explores the transformation of customer service delivery through strategic customization of Oracle Siebel eService self-service portals, demonstrating how enterprises can successfully transition from traditional walk-in centers to fully digital engagement models. The article shows the technical architecture and implementation methodology of Siebel Web Architecture customization, focusing on front-end modifications using CSS, HTML, and JavaScript alongside back-end process optimization strategies that enable seamless integration of mandatory business processes across banking, insurance, and public sector domains. Through comprehensive article analysis, this paper presents a systematic methodology encompassing requirements elicitation, navigation optimization, user interface transformation, and multi-platform deployment approaches—including kiosk and web portal implementations—that resulted in full process completion rates. The article offers quantitative data on radical improvements in customer take-ups, efficient modules, and lowering of costs that occur due to the obviation of physical service center dependence. The results also bring up the best practices of similar implementation, proposing important success factors such as executive sponsorship, agile techniques, far-reaching alteration management, and architectural choices sustaining profitability across all customers within an enterprise-wide deployment, while guaranteeing system performance and user contentment right amid a wide scope of customer demographics.

Aspect-based sentiment analysis for software requirements elicitation using fine-tuned Bidirectional Encoder Representations from Transformers and Explainable Artificial Intelligence

Software requirements engineering is one of the most critical and time-consuming phases of the software-development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, which can jeopardize all subsequent steps in the software-development process and can compromise the quality of the final software product. Natural Language Processing (NLP) is an old area of research; however, it is currently undergoing strong and very positive impacts with recent advances in the area of Machine Learning (ML), namely with the emergence of Deep Learning and, more recently, with the so-called transformer models such as BERT and GPT. Software requirements engineering is also being strongly affected by the entire evolution of ML and other areas of Artificial Intelligence (AI). In this article we conduct a systematic review on how AI, ML and NLP are being used in the various stages of requirements engineering, including requirements elicitation, specification, classification, prioritization, requirements management, requirements traceability, etc. Furthermore, we identify which algorithms are most used in each of these stages, uncover challenges and open problems and suggest future research directions.

Requirements engineering (RE) faces challenges due to the required collaboration and knowledge transfer among analysts, developers, and customers. Motivation theories have occasionally been used to analyze and encourage stakeholder motivation and engagement in RE tasks. Specifically, gamification mechanisms have been applied to software engineering tasks, in particular RE tasks, to promote stakeholder engagement. However, existing research lacks a rigorous method for designing gamified environments for RE tasks. This paper describes the development of a novel ESDR (Elicitation, Specification, Design, and Revision) method that leverages task types, task paradigms, gamification mechanisms, and motivation theories. The model was used as the basis for two case studies, yielding interesting results on student motivation and perceptions towards gamification. For the second case study, a socio-technical environment was developed to allow for gamified requirements elicitation and specification. This environment enables researchers and team managers to select different mechanisms to gamify the current RE task in practice. The environment was evaluated by students, providing anecdotal results. The key contributions are the proposed ESDR method integrating gamification and motivation theories into RE tasks, insights from two case studies applying this method, and the development of a gamified socio-technical environment for RE.

One of the businesses in the service sector that is needed by the community who use motorbikes is a repair shop. The existence of workshops scattered in areas in the city of Mataram often causes people to be confused about finding a workshop that suits their needs. Based on the existing problems, a solution was designed, namely Disera (Digital Service App). DISERA is a service-based digital business to connect automotive experts or repair workers with people who need motorcycle service services. In the DISERA application development process, a structured requirements identification technique is needed. Based on the presentation of the problem and the urgency of elicitation, this research will focus on how to identify the need for the DISERA application as an Android-based motorcycle online service ordering application in the city of Mataram. The elicitation techniques that will be used in this study are survey and prototyping techniques with the aim of obtaining actual user needs and providing prototype designs so that the application development process becomes more focused and produces a good appearance. This research was conducted using a mix of methods, namely qualitative and quantitative methods. Quantitative methods are used as a scheme in collecting data in the form of numbers from survey results to selecting data from observations. While the qualitative method is constructed as a correlative scheme in analyzing problems in the results of case studies and reviewing the basic theory of concept development. In the application design process, data collection is first carried out from primary data obtained through digital surveys, so that user data is obtained to determine the application development process. Prototyping techniques in the software requirements elicitation process can help the process of making application designs that suit user needs. The result is that through survey and prototyping techniques can create application designs that meet user needs

The development of Non-Functional Requirements (NFRs) alongside functional ones has long been the concern of both researchers and software engineers. The main purpose is to derive means to propagate the influence of NFRs throughout the different phases of the software development process. Despite the important progress made through Goal-Oriented Requirements Engineering (GORE) techniques, in terms of requirements elicitation, analysis, and operationalization of goals specifying NFRs, the inherent non-deterministic character of NFRs makes it very hard to anticipate at the requirements phase their impact on subsequent development phases. Thus, at a given phase of development, the actions required to satisfy an NFR, known as Non-Functional actions (NF-actions), depend on various factors, including the actions performed at the previous step as well as the type and nature of the object influenced by the NFR. This study proposes the concept of Complementary Non-Functional Actions (CNF-Actions) to facilitate the analysis of NFRs throughout the Software Development Life Cycle (SDLC). The application of the concept to a theoretical case study of an electronic voting system demonstrated its ability to facilitate the analysis of NFRs while developing the functional requirements and, therefore, contributing to extending the analysis of NFRs to other software phases of the SDLC.

Requirements Engineering (RE) is a well-defined process in software development. Through customer requirement elicitation techniques, Non-Functional Requirements (NFRs) and the Functional Requirements (FRs) that implement them, are defined. However, these techniques are designed for describing deterministic software. Currently, Machine Learning (ML) is becoming ubiquitous, and many domains deploy it. However, ML design and development lacks the formal structure of traditional software development. As ML is developed and deployed in various applications, it would benefit from a structured design and verification process. This paper presents a case study to explore the RE process for ML development. For this case study, the authors follow the RE process for an ML-based software system from customer product description to requirement specification validation. The case study results in 23 NFRs and 151 FRs for the given product. This paper finds that although the NFRs and FRs for ML look different than those for traditional software systems, RE can be adapted for ML development and improves the system descriptions.

Participatory methods have been introduced in healthcare to increase the adoption and acceptance of digital solutions for disease management and health promotion. However, there is not much evidence-based knowledge on how to integrate participatory methods into cohort studies effectively and efficiently. This study reports the results of a health monitoring application requirements elicitation by using a participatory design approach with clinicians and researchers.An electronic survey containing open-ended questions was developed ad hoc in the research group to address requirements and use(r) purposes of the application. Minor amendments were made after piloting the survey with two researchers. A link to the survey was distributed to clinicians and researchers affiliated with the Faculty of Medicine at University of Oulu. All in all, 28 responses were received between April and June 2024. The responses have been analysed with deductive content analysis.The analysis identified 25 themes under the four major themes relating to functional, content and ethical requirements of the application and the use(r) purposes of the application. The contents of the application should consider the physical, mental, social and environmental aspects to address the monitoring of holistic wellbeing. The application was preferred to contain diverse functionalities to track and monitor the user’s health in an automated manner and through user self-reporting. Extensive measures should be made prior and during the application usage to consider ethical requirements of the application. Purposes of use have been identified to include research, health promotion, personalised health management, diagnostics and policy and decision-making aid.The mobile application should be developed to entail a comprehensive outlook on the individual’s current health status by focusing on automated functions, straightforward interface and ease of use to induce attraction. In the future, participatory design activities will be broadened by involving patient and public representatives to the next steps of mobile application development and implementation to further increase the inclusivity and relevance of the application.

ABSTRACTMachine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety‐critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety‐critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research.

ABSTRACTRequirements engineering (RE) is important phase of software development life cycle. Among different RE phases include, requirements elicitation, requirements analysis, requirements specification, requirements validation, and requirements management. There is essential need of an assessment model where software organization can measure their level of capability to implement requirements engineering. Besides so much advances in this field, there is no such assessment model where organizations can find their level of maturity towards requirements engineering process. In this research, requirements engineering model (REM) is designed and implemented via case studies from different software organizations. For designing REM, literature review of different models was conducted, and levels of REM were finalized. In the first phase, success factors of successful software requirements implementation were identified via systematic literature review (SLR). Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). Using Motorola assessment technique, REM was evaluated via case studies from different software organizations. As a result of SLR, total of 50 success factors from different phases of requirements engineering were identified from 191 papers and then mapped to five levels of REM. Three case studies were conducted from different companies to evaluate REM. The outcome analysis of case studies shows that different organizations are on different maturity levels of requirements implementation.