Projects

SWEN

The SWEN Research Group at the University of L’Aquila actively participates in a wide range of projects at the international, national, and industrial levels. Our collaborations aim to address cutting-edge challenges in software development, bringing together academia and industry to innovate in areas such as model-driven engineering, software architecture, performance optimization, and more.

Through partnerships with leading research institutions, companies, and government bodies, our group contributes to groundbreaking research and technological advancements. These projects not only strengthen our expertise but also foster the exchange of knowledge across borders, benefiting both the scientific community and industry stakeholders. By tackling real-world problems, we strive to bridge the gap between theoretical research and practical application, driving the future of software engineering.

International Projects

National Projects

• CN-HPC PNRR Spoke9 (Oct. 2022, Sept. 2025)

Spoke aims to create a faithful digital representation of the social and organizational structures of cities, communities, and their citizens. Through digital twins, the Spoke intends to improve the ability to (i) replicate and understand the functioning and behaviors of our cities and societies, (ii) predict future evolutions, also in response to change, and (iii) support the testing and evaluation of the effects of policies, protocols, and scenarios aimed at changing the behavior of cities and communities.

• PNRR-2022JKA4SL HALO (Sept. 2023, Sept. 2025)

The  HALO project aims to empower users to adjust the autonomy of AI systems to align with their ethical preferences. It proposes a dynamic software exoskeleton that allows users to express moral preferences, such as privacy or dignity, and to control how autonomy is distributed across AI components, software agents, and human users.

• PRIN-2022TEPX4R BeT (Sept 2023 - Sept 2025)

The BeT (Behavior-enabled IoT) project focuses on improving the design and implementation of Internet of Behaviors (IoB) systems, which have emerged due to the increasing integration of IoT, AI, and human-centered technologies. The IoB trend, recognized by Gartner as crucial for businesses to navigate the post-pandemic world, involves collecting and analyzing human behaviors through IoT networks or social platforms to influence user actions.

• PNRR-P2022SELA7 RECHARGE (Nov 2023, Nov 2025)

The RECHARGE project addresses the challenge of automating performance testing in Continuous Integration and Deployment (CI/CD) pipelines by leveraging static analysis and search-based algorithms. The project proposes a novel framework to automate performance testing in CI/CD by introducing automated performance monitoring, which combines static analysis, regression testing optimization, and CI/CD processes to track software performance efficiently over time.

• PRIN-2020W3A5FY EMELIOT (Jun. 2022, Jun. 2025)

Project EMELIOT (Engineered MachinE Learning-intensive IoT systems) studies solutions for engineering highly-dependable, ML-intensive IoT systems. EMELIOT foresees systems where different sensors collect data from physical systems, which in turn can get their status changed by actuators. Collected data are filtered, aggregated, and used to manage ML models locally, on the edge, by dedicated IoT gateways, or on remote cloud servers.

• PNRR-P2022553SL FRINGE (Nov. 2023, Nov 2025)

The FRINGE project provides software engineers, data scientists, and ML experts with a comprehensive set of methodologies, approaches, and software engineering (SE) solutions to improve the development, monitoring, and design of fairness-related properties of ML-intensive systems. FRINGE will help to sustain the digital transformation by empowering advanced artificial intelligence applications through instruments that can lead to the trustworthy and ethical exploitation of large amounts of data. 

• PRIN-2022LKJWHC Trex-SE (Nov. 2022, Nov 2025)

The project TRex-SE (Trustworthy Recommenders x Software Engineers) aims to develop methods to improve the trustworthiness of RSSEs. More specifically, the TRex-SE contributions are related to 1) the development of vulnerability and privacy-aware miners able to create training sets for RSSEs; 2) the conceptualization and instantiation of a trustworthiness model for RSSEs; and 3) the definition and development of trustworthy RSSEs that leverage the aforementioned trustworthiness model and are also able to capture (and leverage) developers’ feedback during development.

Industrial Projects