Abstract Software engineering involves more than coding. It encompasses planning, development, communication, and process management. Scrum, the most widely adopted agile methodology, helps teams deliver value iteratively, yet practitioners often struggle with challenges such as maintaining requirement clarity, reducing cognitive load, and managing communication overhead. As artificial intelligence (AI) becomes increasingly integrated into the software engineering lifecycle, its potential to improve productivity, quality, and decision-making is gaining significant attention. Moreover, Scrum offers a structured yet flexible framework, but it remains unclear which AI features can most effectively support its practices in real-world settings. Therefore, this study addresses that gap by identifying and prioritizing key Scrum AI Support Features (SAISFs) based on industry needs. A two-phase research approach was used. First, a focus group with five software engineering industry experts identified 18 relevant SAISFs. Second, a survey using the Kano methodology was conducted with 344 experienced Scrum practitioners to evaluate and prioritize these features. The results were analyzed across three Scrum team size groups: small ( < = 6), medium (7–10), and large (11+), and four functional SAISF groups: Requirements Support (R), Development Support (D), Communication Support (C), and Scrum Process Support (S). The research also provides prioritization of SAISFs according to Scrum roles. Our findings offer actionable insights for designing AI-enhanced tools tailored to Scrum teams, highlighting the importance of considering team size and Scrum roles when prioritizing AI features. This study contributes to the agile software engineering literature by offering a practitioner-informed foundation for integrating AI into Scrum-based project environments. Future Scrum tools may become adaptive and context-aware, automatically tailoring workflows, predicting bottlenecks, and optimizing team communication and performance.

ABSTRACT Introduction Defining an experimental configuration is a complex decision problem for early‐stage researchers, who must map goals, constraints, and requirements onto datasets, algorithms, and parameter settings that directly affect experimental outcomes. Existing scientific workflow engines improve execution and reproducibility; however, they rarely capture the decision rationale behind configuration choices, which is needed to inform future selections. Method We propose a context‐aware decision‐support framework that formalises experiment configuration as a structured and sequential decision problem. The framework combines three components: a semantic Knowledge Graph (KG) storing historical configurations, contextual attributes, and decision rationale; an MDP‐based Option Explorer that filters the KG under user‐defined constraints and ranks feasible configurations by expected cumulative reward; and a Graphical User Interface for specifying constraints, inspecting ranked alternatives, and providing structured feedback. Unlike existing workflow systems, the framework explicitly separates user‐defined context from automated reasoning, producing an interpretable ranked list rather than a single opaque recommendation. We evaluated the framework in a user study with 90 MSc‐ and PhD‐level researchers performing a model‐selection task, using a synthetic dataset of one million experimental configurations under three levels of contextual detail. Results Compared with manual search, the framework reduced decision time (up to 68%), reduced perceived difficulty (up to 36%), and increased user satisfaction (up to 43%) under the constrained condition. Conclusion By formalising the link between experimental context and probabilistic decision ranking, the framework improves reproducibility and scalability of decision support in scientific experimentation.

Abstract Various smart applications, such as in the domain of smart construction, require the use of artificial intelligence (AI) based services. In order to support such environments, various AI/knowledge service provider and consumer ecosystems have started to emerge. Within such ecosystems, the goals are to improve the quality, reliability, dependability in terms of governance and trust in the data which is exchanged among the various actors, which must be supported by specific business models. This work introduces a novel multi‐party smart contract (SC) that is designed to address the above mentioned goals. Specific service level agreements that illustrate the interactions among the AI service providers and consumers are also presented. The developed multi‐party SC supports different pricing schemes, which are analyzed in detail against the goals of the study.

Summary Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi‐tier applications along edge, fog, and cloud tiers by considering resource properties and non‐functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud‐To‐Things infrastructure based on a subgraph pattern matching (P‐Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud‐native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above.

Databases as software components may be used to serve a variety of smart applications. Currently, the Internet of Things (IoT), Artificial Intelligence (AI) and Cloud technologies are used in the course of projects such as the Horizon 2020 EU-Korea DECENTER project in order to implement four smart applications in the domains of Smart Homes, Smart Cities, Smart Construction and Robot Logistics. In these smart applications the Big Data pipeline starts from various sensor and video streams to which AI and feature extraction methods are applied. The resulting information is stored in database containers, which have to be placed on Edge, Fog or Cloud infrastructures. The placement decision depends on complex application requirements, including Quality of Service (QoS) requirements. Information that must be considered when making placement decisions includes the expected workload, the list of candidate infrastructures, geolocation, connectivity and similar. Software engineers currently perform such decisions manually, which usually leads to QoS threshold violations. This paper aims to automate the process of making such decisions. Therefore, the goals of this paper are to: (1) develop a decision making method for database container placement; (2) formally verify each placement decision and provide probability assurances to the software engineer for high QoS; and (3) design and implement a new architecture that automates the whole process. A new optimisation method is introduced, which is based on the theory and practice of stochastic Markov Decision Processes (MDP). It uses as input monitoring data from the container runtime, the expected workload and user-related metrics in order to automatically construct a probabilistic finite automaton. The generated automaton is used for both automated decision making and placement success verification. The method is implemented in Java. It also uses the PRISM model-checking tool. Kubernetes is used in order to automate the whole process when orchestrating database containers across Edge, Fog and Cloud infrastructures. Experiments are performed for NoSQL Cassandra database containers for three representative workloads of 50000 (workload 1), 200000 (workload 2) and 500000 (workload 3) CRUD database operations. Five computing infrastructures serve as candidates for database container placement. The new MDP-based method is compared with the widely used Analytic Hierarchy Process (AHP) method. The obtained results are used to analyse container placement decisions. When using the new MDP based method there were no QoS violations in any of the placement cases, while when using the AHP based method the placement results in some QoS threshold violations in all workload cases. Due to its properties, the new MDP method is particularly suitable for implementation. The paper also describes a multi-tier distributed computing system that uses multi-level (infrastructure, container, application) monitoring metrics and Kubernetes in order to orchestrate database containers across Edge, Fog and Cloud nodes. This architecture demonstrates fully automated decision making and high QoS container operation.

Many emerging smart applications use sensor data, which are integrated by using various Big Data platforms. Such smart applications must address several requirements including high Quality of Service, privacy and security. Emerging fog computing technologies may provide some new possibilities to address these requirements through the design of multi-tier, container-based applications. In this work, we present the design of a smart application for the domain of civil engineering, which is currently undergoing testing and evaluation.

Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER’s Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.

• Probabilistic method for choosing an optimal cloud deployment option . • Equivalence classification of available cloud deployment options. • Model-checking approach to verify decision-making results. • Experimental study comparing the new probabilistic method with a baseline method. Context : Existing software workbenches allow for the deployment of cloud applications across a variety of Infrastructure-as-a-Service (IaaS) providers. The expected workload, Quality of Service (QoS) and Non-Functional Requirements (NFRs) must be considered before an appropriate infrastructure is selected. However, this decision-making process is complex and time-consuming. Moreover, the software engineer needs assurances that the selected infrastructure will lead to an adequate QoS of the application. Objective : The goal is to develop a new method for selection of an optimal cloud deployment option, that is, an infrastructure and configuration for deployment and to verify that all hard and as many soft QoS requirements as possible will be met at runtime. Method : A new Formal QoS Assurances Method (FoQoSAM), which relies on stochastic Markov models is introduced to facilitate an automated decision-making process. For a given workload, it uses QoS monitoring data and a user-related metric in order to automatically generate a probabilistic model. The probabilistic model takes the form of a finite automaton . It is further used to produce a rank list of cloud deployment options. As a result, any of the cloud deployment options can be verified by applying a probabilistic model checking approach. Results : Testing was performed by ranking deployment options for two cloud applications, File Upload and Video-conferencing. The FoQoSAM method was compared to a baseline Analytic Hierarchy Process (AHP). The results show that the first ranked cloud deployment options satisfy all hard and at least one of the soft requirements for both methods, however, the FoQoSAM method always satisfies at least an additional QoS requirement compared to the baseline AHP method. Conclusions : The proposed new FoQoSAM method is appropriate and can be used in decision-making when ranking and verifying cloud deployment options. Due to its practical utility it was integrated into the SWITCH workbench.