The GOHIT platform is a web-based system designed to support the management of student competitions within higher-education institutions. The first version of the platform lacked structured requirements documentation, resulting in unclear user roles and inconsistent workflows. This study aimed to establish a lightweight yet disciplined requirements-engineering (RE) process for the redevelopment of GOHIT v2. Using a qualitative case-study approach, data were collected through semi-structured interviews and document analysis involving three key stakeholders: the founder, the developer, and the program advisor. Thematic analysis was applied to transform stakeholder inputs into structured, verifiable requirements based on IEEE 830 documentation principles. The process yielded seventeen functional requirements, each traceable to stakeholder sources and internally evaluated using IEEE 830 quality attributes. The resulting Software Requirements Specification demonstrated high levels of completeness, consistency, and traceability, confirming that a structured yet adaptable RE approach can be effective for small or academic development teams. This study contributes a replicable model for implementing lightweight requirements engineering in resource-limited settings and establishes a baseline for future validation and quality assurance using a test-based mechanism.

Ensuring quality in software requirement engineering process: A comparative study

To improve customer satisfaction during the requirement engineering process and create higher consistency in the developed software, there is a growing trend toward the development and delivery of software in an incremental manner. This paper introduces a novel approach to prioritizing the initial development of core subsystems. This prioritization ensures that the most critical subsystems, which contribute significantly to the project’s overall success, are addressed first. Our method involves employing an incremental model with iterative modeling, where each subsystem is assigned a profitability score ranging from 1 to 10. The iterative model is then utilized to identify the most suitable subsystem for the next development stage. The results of our study indicate that utilizing the total profit weight in conjunction with the iterative model effectively identifies the central subsystem of the entire project. This approach proves to be the optimal starting point for development, helping streamline the process and contribute to a more efficient software delivery strategy.

Abstract The proliferation of Internet of Things technologies presents unprecedented opportunities for enhancing sustainability through the principles of the Circular Economy. This research explores the integration of Digital Product Passports within Internet of Things systems to support Circular Economy goals, focusing on the Requirements Engineering perspective. Key objectives include identifying essential requirements for Internet of Things systems, developing a design blueprint, and establishing validation techniques to ensure system compliance with Circular Economy principles and Digital Product Passports integration. The methodology involves a systematic literature review with thematic analysis adapted to the Requirements Engineering process, and the use of FURPS+ and MoSCoW frameworks for categorizing and prioritizing requirements. Findings highlight the critical functional, non-functional, domain-specific, and application-specific requirements necessary for Internet of Things systems to support Circular Economy and Digital Product Passports. The research findings indicate that properly engineered Internet of Things systems facilitate the transition from a linear to a smart digital circular economy by enhancing resource efficiency, minimizing waste, and promoting sustainability.

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.

Recommender systems have become indispensable tools for enhancing user satisfaction and engagement across diverse business sectors, including online marketplaces, streaming services, and e-commerce platforms. This research proposes and evaluates an advanced product review recommendations system that leverages collaborative filtering techniques to deliver personalized and accurate suggestions. By integrating memory-based and model-based collaborative filtering approaches, the system effectively analyses user-item interactions to predict preferences. A key innovation of this study is the application of Singular Value Decomposition (SVD) to decompose the user-item matrix, which not only improves prediction efficiency but also reduces computational demands by addressing data sparsity and dimensionality challenges. The system employs item-based collaborative filtering, utilizing the KNNWithMeans algorithm, and achieves a prediction accuracy of 1.34 RMSE, as validated through rigorous testing on a large-scale electronics product review dataset. Additionally, a correlation-based method is implemented to identify strongly associated products, enabling the generation of highly relevant recommendations. Experimental results demonstrate that the proposed framework outperforms existing recommendation models in terms of scalability and accuracy, particularly for large datasets. Furthermore, this research explores the potential of hybrid models and deep learning techniques to further enhance recommendation quality and mitigate common issues such as the cold-start problem and data sparsity. The findings highlight the system’s robustness in real-world applications and its adaptability to dynamic user behaviour. By combining collaborative filtering with matrix factorization, this study provides a scalable and efficient solution for modern e-commerce platforms seeking to improve user experience and drive sales. Future directions include integrating real-time processing capabilities and exploring advanced machine learning algorithms to refine recommendation precision. References Afshar, M. Z. (2023). Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO). International Journal of Emerging Issues in Social Science, Arts and Humanities, 2(1), 1-10. https://doi.org/10.60072/ijeissah.2023.v2i01.001 Afshar, M. Z., & Shah, M. H. (2025). Performance evaluation using Balanced Scorecard framework: Insights from a public sector case study. International Journal of Human and Society, 5(1), 40-47. https://ijhs.com.pk/index.php/IJHS/article/view/808 Afshar, M. Z., & Shah, M. H. (2025). Strategic evaluation using PESTLE and SWOT frameworks: Public sector perspective. ISRG Journal of Economics, Business & Management, 3(1), 108-114. https://doi.org/10.5281/zenodo.14854362 Aggarwal, C. C. (2016). Recommender systems: The textbook. Springer. Anjum, N., & Kabir, A. (2019). Introducing Refined Agile Model (RAM) in the context of Bangladesh's software development environment concentrating on the improvement of requirement engineering process. International Journal of Software Engineering & Applications, 10(4). Badrul, S., Konstan, J., & Riedl, J. (2005). Combining collaborative filtering with personal agents for better recommendations. Proceedings of the 4th International Conference on Intelligent User Interfaces (pp. 285-295). Bell, R., & Koren, Y. (2007). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Proceedings of the 7th IEEE International Conference on Data Mining (pp. 43-52). Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (pp. 43-52). Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems (pp. 191-198). Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., et al. (2010). The YouTube video recommendation system. Proceedings of the 4th ACM Conference on Recommender Systems (pp. 293-296). Dhal, K., Karmokar, P., & Chakravarthy, A. (2022). Vision-based guidance for tracking multiple dynamic objects. Journal of Intelligent & Robotic Systems, 105(66). https://doi.org/10.1007/s10846-022-01657-6 Funk, S. (2006). Netflix update: Try this at home. https://sifter.org/~simon/journal/20061211.html Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), 1-19. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web (pp. 173-182). Koren, Y., & Bell, R. (2015). Advances in collaborative filtering. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 77-118). Springer. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76-80. Rendle, S. (2010). Factorization machines. Proceedings of the 10th IEEE International Conference on Data Mining (pp. 995-1000). Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). 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The software development life cycle places requirement engineering (RE) at its core because of its fundamental importance. Global Software Development (GSD) creates significant difficulties for requirements collection and implementation because it faces challenges including geographical distance and time zone differences and language barriers and cultural differences. Due to difficulties in traditional requirements collection and analysis procedures in GSD projects organizations need to advance their RE process. Over several investigations’ researchers have established that RE process maturity operates as a critical determinant for achieving successful requirements implementation in GSD. The research identifies every potential practice to enhance RE process maturity by analyzing literature through Systematic Literature Review (SLR). The Systematic Literature Review revealed that RE process maturity includes 20 distinct practices as its outcome.

This paper presents a metamodeling approach to address the lack of methodological support in canvas model development, focusing on planning critical IoT systems. We introduce MM4Canvas - a metamodel that provides a solid foundation for developing structured and standardized canvas models, allowing for consistent reuse and extension across diverse project types. A proof of concept was conducted by instantiating a general-purpose canvas model based on MM4Canvas for project planning, aiming to establish a connection between this activity and the Requirements Engineering process. We extended this model to incorporate safety and security requirements for critical IoT systems. Our contribution demonstrates the metamodel’s capacity to support standardization, reuse, and extensibility in canvas-based project planning.

In the dynamic landscape of software development, open source software (OSS) has acquired traction due to its collaborative and transparent nature. In OSS, source code of the software is accessible to users under a copyright license. The license allows the users to examine, improve, alter and distribute the software either in modified or unmodified form. Richard Stallman, programmer at MIT, presented the idea of making source code freely accessible in the year 1983, from an ideological movement. Users must accept the conditions of a license before the use of OSS. Requirement engineering (RE) is the process of gathering requirements statements, along with the documentation and analysis for a project. Requirements that are insufficient, unclear, inconsistent, and incomplete, may adversely affect software development. In OSS, requirements emerge through online conversations, as well as discussions evolve continually in different online forums and emails. Requirements’ gathering is a complex process and its practitioners may face different challenges while practicing it. A Multivocal literature review (MLR) will be conducted to find out challenges, success factors, and practices/solutions for the identified challenges in RE process of OSS. This research will help academic researcher, OSS practitioners and managers to improve RE process, leading to more efficient, effective and successful software applications.

Internet of Things (IoT) is one of the growing technologies embedded in most application systems. It aims to solve real-world problems in different environmental fields such as industry, education, healthcare etc. IoT is becoming an integral part of daily devices and technologies opening a need for efficient and novel solutions to meet functional requirements that are more complex than those in traditional requirement engineering (RE). In addition, remotely smart systems present new challenges and lack in RE process that needs a solution. IoT systems open new research issues in RE such as elicitation, analysis, specification and management of IoT RE. To solve this lack of RE new smart techniques based on AI must be applied in the elicitation RE process. This paper presents a new smart dynamic approach in the RE elicitation phase to build dynamic functional requirements based on AI models. New stakeholder expectation needs from the smart IOT system are collected and stored in the requirements dataset. These new requirements are analyzed and classified into requirement features using the Support Vector Machine classifier. These classified functional requirements are compared to the IoT system, and the positive training requirements are added to the smart functional requirement presented in the IoT system. The proposed approach shows a significant accuracy of 95.64%, where 395 features were classified and detected from 413 entered features. This paper measures the gap between stakeholder expectations and device requirements in smart systems; these proposed measures can be implemented to optimize smart device specifications for manufacturers.

Accessible tourism has become relevant, generating significant economic and social impacts. Even though the accessible tourism market is rising and presents an excellent business opportunity, this market is largely ignored, as it is challenging to stimulate the flow of accessibility information. Accessible technologies, such as tourism information systems, can be a potential solution, increasing accessibility through communication. However, these solutions are few and tend to fail the integration of users upon development processes. This research aims to present a technological platform to improve accessibility in the tourism industry. The name of this accessible and adaptable technological solution is access@tour by action, and it was created following a user-centered design methodology. This development involved a requirement engineering process based on three crucial stakeholders in accessible tourism: educational institutions, supply agents, and demand agents. The design phase was achieved with the help of a conceptual model based on a unified modeling language. The initial prototype of the solution, created in Adobe XD, implements a wide range of informational and accessibility requirements. Some access@tour by action interfaces outline the design, content, and primary functionalities. By linking technological development, tourism, and social inclusion components, this study highlights the relevance and interdisciplinarity of processes in developing accessible information systems.

This teaching case explores the digital transformation journey of CHRIST Juweliere und Uhrmacher seit 1863 GmbH (CHRIST), a historic jewelry company adapting to a digital retail environment. CHRIST has partnered with the consultancy KPS AG to enhance its e-commerce and marketing capabilities using agile methodologies, specifically the Scrum framework. A central focus is the requirement engineering process, which ensures the alignment of business needs with technical solutions. This teaching case examines how CHRIST identifies, documents, and refines user stories to create a structured product backlog. It highlights the collaboration between business and development teams to clarify requirements, set priorities, and manage scope changes. It delves into how CHRIST and KPS collaboratively manage the intricacies of digital transformation, ensuring alignment across multiple project streams and leveraging external expertise to drive innovation. Students will gain insights into the complexities of requirement engineering, the benefits of agile project management, and the strategic approaches necessary for successful digital transformation in a multi-channel retail business.

Integrating environmental features into software requirements during the requirements engineering (RE) process is known as sustainable requirements engineering. Unlike previous studies, we found that there is a strong relationship between nonfunctional requirements and sustainable environmental factors. This study presents a novel methodology correlating nonfunctional requirements (NFRs) with precise, sustainable green IT factors. Our mapping methodology consists of two steps. In the first step, we link sustainability dimensions to the two groups of green IT aspects. In the second step, we connect NFRs to sustainability aspects. Our proposed methodology is based on the extended PROMISE_exp dataset in combination with the Bidirectional Encoder Representations from Transformers (BERT) language model. Moreover, we evaluate the model by inserting a new binary classification column into the dataset to classify the sustainability factors into socio-economic and eco-technical groups. The performance of the model is assessed using four performance metrics: accuracy, precision, recall, and F1 score. With 16 epochs and a batch size of 32, 90% accuracy was achieved. The proposed model indicates an improvement in performance metrics values yielding an increase of 3.4% in accuracy, 3% in precision, 3.4% in recall, and 16% in F1 score values compared to the competitive previous studies. This acts as a proof of concept for automating the evaluation of sustainability realization in software during the initial stages of development.

Data protection compliance is critical from a requirements engineering (RE) perspective, both from a software development lifecycle (SDLC) perspective and regulatory compliance. Not including these requirements from the early phases of the SDLC can prove costly and challenging afterward. The general data protection regulation (GDPR) from the European Union (EU) sets a list of requirements that organizations working within its scope should satisfy. However, these requirements are complex to work with, as legal prose tends to be vague and imprecise, and not all requirements have received the same attention from researchers. This study aims to identify the research published in RE for helping compliance with regulatory data protection requirements. We gathered and analyzed 90 articles from 2016 to 2022 through a systematic mapping study. We analyzed key trends in the sample, such as year of publication, publication venue, type of research, interdisciplinarity in the author’s background, GDPR focus of compliance element, and type of proposal. Our main findings show ongoing interest, mostly published in conferences, in achieving overall compliance with the GDPR and consent as the most popular topics. Other topics, such as cookies or children’s data, did not receive significant attention. Research over the whole RE process has been done. 20 (22%) of the papers have authors affiliated with non-computer science; however, most research seems not interdisciplinary. We finally discuss gaps in the literature, possible future areas of research, and the importance of interdisciplinary research for regulatory data protection requirements in RE.

Introduction/Importance of Study: Industrial-scale software development tends to create more business value and effective strategic capabilities in software industries. IT organizations are spending about 50% of the budget on software development to buildfaster software programs at minimal cost to achievesuccess in industrial-scale projects. The crucial part of developing industrial-scale software is deciding ‘what is intended to be built’. If the problem is not tackled properly, this can result in serious errors that impact the entire Software Development Life Cycle (SDLC) and make it difficult and costly to repair in later stages. Similarly, challenges in industrial-scale development related to Requirementsare complex including Requirementscope, elicitation, specification, validation, and management. The Requirementengineering challenges become bigger and harderto overcome in industrial-scale projects due to time and cost factors. The money spent on Requirementchange may affect the overall development time of the project. The complexity of industrial-scale projects does not increase linearly, thus, impactingthe development process. Novelty Statement: Therefore, the need to address challenges in large IT projects comes with the reason of their economic value in local and international markets. Researchers have come up with the identification of challenges, but their studies lack the overall Requirementengineering process. There is a need to design a comprehensive solution to overcome the Requirementengineering challenges that contribute to project failure. Material and Method: Therefore, the research is divided into three phases: “The Identification Phase”, where the project challenges would be identified; “The Implementation Phase”, where these factors would be shortlisted to design a framework; and “The Validation Phase”, in which validation of the framework would be done using triangulation technique. Result and Discussion: The outcomes will focuson facilitatingthe software development industry foraddressing the Requirementofengineering challenges in industrial-scale projects to reduce the chances of failure.

Abstract We have a unique opportunity to consider justice in our design of a cleaner energy system. This paper introduces the Justice-Embedded Requirements Engineering (JERE) process, which was created to enable engineers to consider project goals, requirements, and potential project impacts on historically marginalized, climate-vulnerable communities. Given JERE's focus on energy technologies, we demonstrate the process using a concentrating solar power example. JERE provides engineers with a tool to better ensure justice is embedded in the system design process from the beginning.

This study investigates the processes and means of requirements engineering (RE) of software and complex information systems (SWS). The subject of this study is the instrumental means of SWS RE based on the methods of artificial (computational) intelligence (AI). The goal is to improve the accuracy and efficiency of CCD requirements development processes using AI tools by providing better communications between business teams and technical teams and automating complex requirements collection and documentation processes. The tasks are as follows: to analyze the principles and means of integrating AI tools into requirements engineering processes, the development and verification of which is a critical stage in the development of SWS; to determine the problems of using traditional SWS RE methods and perform their comparative analysis with AI-based methods; and to develop a chatbot architecture for requirements validation and identify characteristics of RE processes that are improved through its use. The results. The results of this study demonstrate that AI tools can significantly improve the accuracy, timeliness, and efficiency of requirements development processes and project team communication. An architecture and software solution for an intelligent chatbot for requirements validation is proposed, and the benefits and limitations of its application are discussed. Directions for further development and research regarding the end-to-end implementation of AI tools in IP processes have been developed.

Requirements Elicitation for a Flexible, Digital, and Methodological Manufacturing Change Management based on a Literature Review and Expert Interviews

Privacy requirements engineering is a crucial aspect of privacy engineering. It aims to integrate privacy principles into organizational and technical processes throughout the software development lifecycle. This specialized field involves various strategies, including compliance with regulatory frameworks, asset analysis, and system diagram development for threat modeling. The wide range of approaches, while beneficial in providing different perspectives, presents a significant challenge to the novice privacy engineer or developer in identifying the most effective methodologies. The lack of a single methodology highlights the need for a systematic literature review (SLR) to establish a standardized process for privacy requirements engineering that promotes consistency across different methodologies. To address this issue, we conducted a comprehensive SLR to synthesize existing privacy requirements engineering methodologies. Our analysis involved dissecting each method's processes, tasks, techniques, work products, and resources. Our review examined 40 privacy requirements engineering methodologies detailed in 50 papers, from which we extracted five key processes commonly followed in privacy requirements engineering research. We used this as the foundation for a holistic approach to facilitate the adoption of a comprehensive privacy requirements engineering process. The review also identifies ongoing challenges and suggests future directions in this field. INDEX TERMS privacy requirements engineering, privacy design engineering, privacy engineering, privacy by design

ABSTRACTToday, requirements engineering (RE) is a key process in the development of complex systems‐(ISO/IEC/IEEE 2015). Requirements containing quality issues such as ambiguity or inconsistency can lead to late error detection in systems design, resulting in high project cost overruns. This paper presents challenges for early system requirements verification and validation associated to an executable model‐based requirements engineering (eMBRE) process proposal.