Jun 2026· Engineering Interactive Computing System· pp. 23-31· 0 citations· 48 references
Computer Science
TL;DR
The results indicate that GenAI tools have the potential to support the initial generation and review of requirements, reducing the initial documentation effort, however, supervision by requirements engineers remains essential.
Abstract
IoT software systems involve implicit and explicit interactions among humans, devices, and environments, which makes requirements specification and evaluation more complex and context-sensitive. Large Language Models (LLMs) emerge as potential tools to support Requirements Engineering (RE) activities, although empirical evidence on their application in IoT software systems remains limited. In this article, we analyze how Generative Artificial Intelligence (GenAI) tools can support the specification and quality evaluation of requirements in IoT software systems through a two-stage application study. First, we carried out a Rapid Review of the Scopus database to identify GenAI tools used in RE. Next, we conducted an experimental study in a real-world setting to develop a smart home system to support a person with physical disabilities. We used ChatGPT to generate (i) requirements from a vision document and a predefined specification structure, and (ii) the evaluation of the requirements against ISO/IEC/IEEE 29148 criteria. The model generated an initial specification containing 35 requirements (23 functional and 12 non-functional) and provided suggestions for improvement during the quality evaluation phase. Only a subset of the requirements required additional adjustments by the engineers. The results indicate that GenAI tools have the potential to support the initial generation and review of requirements, reducing the initial documentation effort. However, supervision by requirements engineers remains essential. As an additional contribution, this study proposes the SpecEval-IoT environment to automate the specification and evaluation of requirements for IoT software systems.
This work presents an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications that combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency.
Denesa Zyberaj, Roman Vintonyak, Pascal Hirmer et al.· 0 citations
Internet of Things (IoT) and edge systems induce complex, heterogeneous, and distributed environments where interconnected physical devices and localized computing resources exhibit intricate behaviors. Specifying behavioral requirements in such systems is inherently challenging. Temporal logic, notably Linear Temporal Logic (LTL), is widely adopted for their formal specification, serving as a critical precursor to, e.g., verification or synthesis. This paper systematically investigates scientific literature spanning the past decade. We collect requirements and their corresponding LTL properties, analyzing trends in LTL specification within edge/IoT systems over time. Subsequently, we automatically extract defining features of these requirements and properties, including formula complexity and natural-language characteristics, and analyze their correlations. The insights we derive offer a valuable resource for researchers and practitioners engaged in reasoning within edge/IoT systems. Beyond serving as a reference corpus, the dataset can aid evaluation and validation of other approaches developed by the formal methods community, or serve as training data for machine learning techniques targeting the formalization process.
Angeliki Pantiora, Roman Bögli, Timo Kehrer et al.· Annual International Compute...· 0 citations
The Cisco Packet Tracer (CPT) is a network and IoT systems design and simulation tool developed by Cisco Systems inc. The tool is widely used in academia and the industry to develop and simulate complex IoT systems for training purposes. Although the graphical notations of CPT are appealing to system developers, the tool does not support formal verification. Therefore, CPT is limited when it comes to the design and development of safety and secutiry critical IoT systems where faults can endanger people's lives or damage properties and businesses. This paper proposes a formal approach to the verification of IoT system models designed in the CPT tool, based on the Calculus of Context-aware Ambients (CCA). First, the graphical notations of CPT are mapped to processes in CCA that specify their behaviours. Then any desired property of a system can be specified as a Linear Temporal Logic (LTL) formula. Finally, the CCA Runtime Verification tool ccaRV is used to verify formally whether the behaviour of an IoT system model designed in CPT violates the system's requirements. The pragmatics of the proposed approach is illustrated using a case study.
François Siewe· Annual International Compute...· 0 citations
Generative Artificial Intelligence (GenAI) is increasingly integrated into software products to enable new features and user capabilities, from early exploration to operational deployment. GenAI adoption as a component within a software system introduces quality risks because GenAI outputs are probabilistic, prompt-sensitive, and may drift after release. Organizations, therefore, need to decide what to evaluate, when to evaluate, and who owns quality evaluation activities across software design, development, and operations. ISO/IEC 25059 standard distinguishes between software product quality (e.g., usability) and quality-in-use (e.g., satisfaction) for AI-enabled software, yet it provides limited operational guidance for these evaluation activities. We therefore investigate how industrial software teams adopt and use GenAI models in the software systems they build and operate, and how they evaluate system qualities when deciding to adopt GenAI during development and after deployment. We do not benchmark the underlying GenAI model itself. In this study, we conducted 19 semi-structured interviews in two software development companies. We triangulated the interviews with archival data (15 internal documents and 184 internal wiki/web pages) to capture GenAI adoption steps, quality concerns, evaluation practices, and role responsibilities. Our findings describe a three-phase adoption process – Ideation, Development, and Operation – highlighting where quality evaluations occur, which criteria are used, and how evaluation responsibilities are distributed. Based on observed practices and using ISO/IEC 25059 as an organizing lens, we synthesize a process-oriented quality evaluation framework. This framework maps metrics to explicit gatekeeping, validation, and monitoring checkpoints, bridging abstract ISO quality characteristics with engineering workflows. We applied the framework in a GenAI-enabled software product (SE4AI) use case and reported how it supported structured evaluation activities. We also observed that quality evaluations span legal, security, development, QA, and operations, but ownership is fragmented across phases. We therefore propose a GenAI Quality Lead responsibility (often assignable to an existing senior role) to coordinate criteria, evidence, and traceability across quality evaluation activities. The results contribute to Software Engineering for AI (SE4AI) by clarifying how teams can measure qualities when building software that adopts and uses GenAI.
Liang Yu, Emil Alégroth, Panagiota Chatzipetrou et al.· IEEE Transactions on Softwar...· 0 citations
Smart campus development is increasingly associated with the combined use of IoT technologies, artificial intelligence, cloud infrastructures, and large-scale data analytics in higher education. Despite this progress, many existing data models are not well-suited to the educational domain, particularly when interoperability and real-time analytical capabilities are required. To address this limitation, the study proposes a Smart Campus Education Data Model (SCEDM), which can be integrated into any FIWARE-based platform. The model is organized as a layered architecture that includes data acquisition, processing, and storage; analytics and decision support; application presentation; and security. The proposed model is not presented only at a conceptual level; it is also validated in a con-tainerized FIWARE environment built around the Orion-ld Context Broker and NGSI-ld specifications. The SCEDM model is validated in a system that supports real-time state management across multiple campus domains. The model’s practical operation is validated across several experimental scenarios, including a simulation of a lecture process, classroom occupancy monitoring, and automated notifications to external platforms. In addition, the study compares five international case studies from different contexts. The comparison shows that, despite differences across local settings, similar benefits can be observed in campus operations and learning conditions. The study also recognizes several continuing challenges in the development of smart campuses, including interoperability, long-term scalability, data governance, privacy protection, stakeholder engagement, and financial sustainability. In response to these issues, the authors propose practical design guidelines alongside strategic recommendations for adoption at the institutional level.
G. Nedeltcheva, Tsvetelin Stefanov, D. Chikurtev et al.· TechSys 2026· 0 citations
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.
Bruno Carvalho da Silva, Rodrigo Pereira dos Santos, Davi Viana· Requirements Engineering· 0 citations