ReqCluster4IoT is proposed, a method for clustering functional requirements in IoT applications that helps practitioners identify how related functionalities are organized into clusters, reveal relationships among requirements, and associate each cluster with IoT-specific characteristics, thereby supporting requirements analysis, implementation planning, and requirements management.
Abstract
Requirements engineering is crucial for software development. Analyzing large sets of requirements in natural language is difficult and time-consuming, especially in Internet of Things (IoT) projects, where heterogeneous devices, services, communication infrastructures, and environmental constraints increase the complexity of the requirements. In this context, clustering can support requirements analysis by organizing functional requirements into semantically coherent groups that represent the same functional aspect or subsystem. This helps analysts reduce manual effort, identify relationships and dependencies between requirements, structure large sets of requirements, and better understand system responsibilities and specific IoT concerns. For these reasons, we propose ReqCluster4IoT, a method for clustering functional requirements in IoT applications. The method builds on a focused exploration of the literature to identify IoT characteristics, along with experiments on requirement classification and text semantic similarity. We evaluated the method’s acceptance and usefulness through an online questionnaire and a focus group. The online questionnaire collected feedback from 10 IoT software developers, whereas the focus group provided more in-depth insights into the method’s results. Findings suggest that ReqCluster4IoT can capture meaningful relationships among requirements and generate clusters that practitioners perceive as useful. Additionally, the method helps practitioners identify how related functionalities are organized into clusters, reveal relationships among requirements, and associate each cluster with IoT-specific characteristics, thereby supporting requirements analysis, implementation planning, and requirements management. Overall, participants reported positive acceptance of the method regarding its usefulness, its potential to reduce effort, and their intention to use it.
The results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real world scenarios, while remaining sensitive to CQ specificity and modelling profile.
Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid et al.· 1 citation
This article examines the processes for ensuring the information security of client interfaces (Front-end) in Internet of Things (IoT) systems. This study aims to develop and implement an automated DevSecOps pipeline integrated into the Gulp build environment to detect and block code vulnerabilities at the earliest stages of development (the Shift Left concept). The tasks are as follows: 1) analyze technological opportunities and challenges of implementing DevSecOps for IoT web interfaces; 2) analyze possible threats and vulnerabilities inherent in client-side code; 3) analyze existing approaches to integrating SAST tools into automated workflows; 4) analyze options for using standard automation tools (such as Gulp) to solve security tasks; 5) propose a DevSecOps pipeline architecture for the security of client interfaces; 6) propose a sequence of critical components and experimentally verify the effectiveness of the proposed solution. Based on the set tasks, the following results were obtained. An analysis of security problems in modern JavaScript-based IoT interfaces was conducted, and the need for automated code control was substantiated. An architecture for a DevSecOps pipeline is proposed using Gulp.js as an automation tool and a configured ESLint as a SAST scanner. A Gulp plugin ("wrapper") has been developed and implemented, providing continuous code monitoring during development. The system successfully identifies dangerous patterns (e.g., the use of eval functions or vulnerable setTimeout constructions) and blocks such code from entering the final release (Artifact). The proposed approach does not create significant overhead on the development process but guarantees compliance with security policies. Conclusions. The main contribution and scientific novelty of the results lie in creating an adaptive, easily integrated protection mechanism for the IoT ecosystem by integrating Static Application Security Testing (SAST) tools directly into the Gulp task runner architecture. Applying DevSecOps practices at the build level minimizes human-factor risks and increases overall trust in smart device management systems. The proposed solution is scalable and can serve as a foundational element of a cybersecurity strategy for Internet of Things projects
A. Antonenko, Oleh Bondarenko, O. Golubenko et al.· RADIOELECTRONIC AND COMPUTER...· 0 citations
: Nowadays, software projects have become extremely large-scale, and their development requires the application of numerous standard and innovative technologies. Hence, the possibility of embedding and maintaining information about the mutual dependencies of the components in the software system becomes one of the key and necessary activities during development. This traceability feature enables the monitoring of the artifact connections and helps in preserving the system integrity. First, this paper briefly introduces the concept itself and the three most relevant groups of traceability methods: IR-based, ML-based, and graph-based. Then, the paper focuses on evaluation data from the available literature that provide an insight into the comparative performance of the approaches. The basic metrics observed were: standard (Precision, Recall, and F-score), and specific ones. Different evaluation setups and configurations are also considered. Although some papers indicate that ML-based methods can outperform IR-based ones, no general conclusion can be drawn because of quite different datasets and evaluation environments.
Vojislav Tomašević, Jelena Protic, Maja Vukasović· SINTEZA· 0 citations
Requirement engineering is a foundational stage of the software development life cycle, and the accuracy with which
requirements are classified directly influences downstream design, testing, and cost estimation. Software requirements are
commonly separated into Functional Requirements (FR), which describe what a system must do, and Non-Functional
Requirements (NFR), which describe how well the system must do it, covering attributes such as performance, security, and
usability. When this separation is carried out manually, the process is slow, subjective, and prone to disagreement between
analysts, particularly as project size grows. This paper presents a Requirement Classification and Prioritization Tool that
combines Natural Language Processing (NLP) with machine learning to automate the FR/NFR decision. Requirement
statements are cleaned and normalised, then represented numerically through two complementary techniques: Term FrequencyInverse Document Frequency (TF-IDF) and contextual BERT embeddings. Four classifiers-Logistic Regression, Support Vector
Machine, Random Forest, and a
BERT-based model-are trained and benchmarked on a dataset of 6,086 labelled requirement statements using accuracy,
precision, recall, and
F1-score. A Gradio-based interface allows a requirement to be submitted and its predicted category, confidence score, and a short
explanation to be viewed immediately. The results indicate that transformer-based representations offer a modest but consistent
improvement in contextual understanding over TF-IDF, while classical classifiers remain competitive and considerably cheaper
to train.
Sai Sindhuja Bhukya, Naveen Kumar Nuthanapati· International Journal for Re...· 0 citations
This paper builds on the author’s previous work regarding domain-specific ontologies (DSO) and its importance in the human-factors integration (HFI) space. Explicit term definitions captured by a DSO allow the HFI vocabulary to be mapped into a model-based enterprise architecture (MBEA). Integrating this terminology into the overall MBEA provides insight into the role that individuals play by considering personnel as a critical system component. Often considered external actors, human resources are typically not accounted for in the original solution design. However, MBEA promises to reverse this trend by implementing the Unified Architecture Framework (UAF). The UAF is composed of various domains and their aspects and is meant to graphically illustrate enterprise concepts such as strategy, operations, resources, personnel, and services in a digital environment. Capturing HFI information in a model improves the traceability of person(s) and organizational concerns, responsibilities, and competencies to highlight gaps that must be addressed. The incorporation of the HFI DSO into an MBEA enhances communication between disciplines and provides transparency for stakeholders. This research demonstrates the feasibility of constructing a DSO based on an HFI body of knowledge; leveraging the Web Ontology Language (OWL), the subject-predicate-object (SPO) approach, and the Protégé ontology editor. It also shows that by importing the OWL file into a concept model, understanding HFI terms facilitates MBEA while maintaining personnel as a critical part of a successful organization. This research identifies areas for improvement of the UAF domain-specific modeling language (DSML) to ensure that it adequately addresses HFI concerns by mapping like-terms.
The increasing complexity of Internet of Things (IoT) applications has exposed the limitations of monolithic software architectures in addressing scalability, flexibility, and real-time processing requirements. Although microservice architectures offer a promising alternative, identifying optimal service boundaries remains a significant challenge, often resulting in excessive inter-service communication and degraded system performance when poorly defined. This study proposes a quantitative model for refactoring monolithic applications into microservices by integrating Domain-Driven Design (DDD) principles with measurable metrics, including service size, coupling, and scalability. The model systematically identifies optimal service boundaries through a structured evaluation framework. The proposed approach is validated using a case study of an IoT-based irrigation management system. Experimental results show a reduction in inter-service communication overhead and improved modularity and scalability compared to baseline decomposition approaches. The findings demonstrate that combining DDD concepts with quantitative analysis provides an effective and practical solution for guiding microservice migration in complex IoT environments. The average coupling score across the refactored system was recorded at 20.4%, which satisfies the theoretical requirement of remaining below 30% and aligns with empirical observations from successful microservice decompositions.
Munezero Immaculée Josélyne, Ngenzi Alexander, Hitimana Eric et al.· International Journal of Adv...· 0 citations