Context: Dashboards play a pivotal role in cloud systems monitoring, as they facilitate the visualization of the Key Performance Indicators (KPIs) that are continuously gathered from the system under observation. To timely and easily identify malfunctions and unexpected behaviors, operators have to configure, design, and maintain dashboards, so that the right set of indicators is properly visualized. Unfortunately, cost-effectively manipulating dashboards is a challenge, also for experts. Objectives: This paper proposes a model-driven approach that supports both the cost-effective definition (generation) and modification (adaptation) of dashboards. Method: The key idea is that a model-driven representation of a dashboard can be more easily manipulated than interacting with the GUI of dashboard management systems. Once a dashboard’s model is defined, the actual dashboard can be generated automatically with model-transformation techniques. Results: Our empirical results with popular Grafana Labs and Dynatrace dashboards show that the interpretability of the dashboards generated automatically is similar to the one of the manually configured dashboards. Moreover, the model-driven customization of the dashboard allows non-expert operators to act more efficiently, sometime as efficient as expert users. Conclusions: Overall results show that the model-driven approach can be used to cost-effectively generate useful dashboards, with an effectiveness close to that of experts.

A few years after their release, Large Language Models (LLMs)-based tools are becoming an essential component of software education, as calculators are used in math courses. When learning software engineering (SE), the challenge is the extent to which LLMs are suitable and easy to use for different software development tasks. In this paper, we report the findings and lessons learned from using LLM-based tools-ChatGPT in particular-in five SE courses from four universities. After instructing students on the LLM potentials in SE and about prompting strategies, we ask participants to complete a survey and be involved in semi-structured interviews. The collected results report (i) indications about the usefulness of the LLM for different tasks, (ii) challenges to prompt the LLM, i.e., interact with it, (iii) challenges to adapt the generated artifacts to their own needs, and (iv) wishes about some valuable features students would like to see in LLM-based tools. Although results vary among different courses, also because of students' seniority and course goals, the perceived usefulness is greater for lowlevel phases (e.g., coding or debugging/fault localization) than for analysis and design phases. Interaction and code adaptation challenges vary among tasks and are mostly related to the need for task-specific prompts, as well as better specification of the development context.

Digital twins, as virtual models reflecting physical entities, serve multiple purposes, supporting the understanding, design, and management of systems, across various domains. The concept of digital twins has been also applied to smart cities, coining the term “Urban Digital Twin”. Indeed, smart cities continuously produce data that can serve as a real-time feed for their digital twin representations. However, developing urban digital twins is challenging as the domain is complex and evolving rapidly. Moreover, existing digital twin platforms are mostly generic platforms, limiting their adoption for a specific context such as smart cities. The aim of this study is to overcome the limitation of existing general-purpose digital twin platforms, particularly in the context of smart cities, by supporting the development and evolution of urban digital twins. To this aim, we propose a low-code assessment platform for smart cities represented as urban digital twins by leveraging distributed runtime models to implement and deploy service-based quality evaluation systems. The innovative platform and its architecture enable the creation of efficient urban digital twins, allowing for their continuous evolution without requiring redeployment whenever the digital twin definitions are modified. Experimental evaluations demonstrate the effectiveness and efficiency of the proposed platform, highlighting its potential for evolving urban digital twins in smart cities quality assessment. Results show evidence that combining distributed runtime models with the advantages of low-code platforms is beneficial within the smart cities domain. Moreover, this study underscores the significance of specialized platforms tailored to the smart cities domain.

Littering is a major problem that threatens the environment, society, and economy. Keep track, monitor and regularly clean littering sites can be a crucial problem that involves public authorities, municipalities, companies, and citizens. So far approaches have not well leveraged the knowledge and capabilities that derive from the federation of multiple communities, such as cities, public bodies, and organizations. In this paper, we describe the COBOL project, a National PRIN (Progetti di Rilevante Interesse Nazionale) PNRR (Piano Nazionale Ripresa e Resilienza) project funded by the Italian MUR (Ministero dell'Università e della Ricerca) in 2023. The project aims to definite a flexible framework for managing the waste disposal process through a federated learning architecture that collects and integrates the reports (e.g., annotated pictures and user feedback) shared by the communities involved in the waste disposal process. To deliver an advanced waste disposal service based on the direct participation of citizens, COBOL also integrates Model-Driven Engineering principles, Computer Vision techniques, and Self-Adaptation mechanisms. Early results show that reports can be effectively collected and processed with COBOL.

In the model-driven security domain, access control systems provide an application for handling access of persons through controlled gates. A gate, such as a door, can have a lock mechanism for securing the area from unauthorized access. Most commercial solutions for access control management offer pre-packaged software systems where customization of the authorization logic is either not allowed or subject to payment. Moreover, cross-platform development is a barrier for solution providers due to the high cost of development and maintenance that it implies. To overcome these limitations and further optimize the entire access control systems development process, we propose a model-driven approach that supports automatic code generation to enable communication between an IoT infrastructure and platforms for Facility Access Management. Specifically, the approach combines the benefits of Near-Field Communication (NFC) and Tinkerforge (i.e., an open-source hardware platform) with model-driven techniques. This allows the approach to exploit both behavioral and structural models for the modeling and the consequent code generation of part of the authorization mechanism, thus providing complete coverage of the code generated for the whole system. We implemented and evaluated our approach in a real-world case study within the premises of a fitness center with an IoT infrastructure consisting of several heterogeneous sensors by showing its practical applicability. Experimental results demonstrate the effectiveness of our approach in supporting abstraction and automation concerning traditional code-centric development through code generation features. Consequently, our approach makes the whole development process less time-consuming and error-prone, thus reducing the system's time to market.

Abstract Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework calledMIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.