Leonardo SCOMMEGNA

Ruolo attuale:: Ricercatore Legge 240/10 a tempo determinato
SSD:: IINF-05/A - Sistemi di elaborazione delle informazioni
Afferenza organizzativa:: Dipartimento di Ingegneria dell'Informazione

Recapiti

leonardo.scommegna(AT)unifi.it

Leonardo SCOMMEGNA

Biografia

Ha conseguito la Laurea magistrale in Ingegneria Informatica presso l'Università di Firenze nel 2019, e il dottorato di ricerca in Smart Computing nel 2023.
È attualmente ricercatore presso il Dipartimento di Ingegneria dell'Informazione dell'Università di Firenze e membro del Software Technologies Lab.
I suoi interessi includono:
Model-Driven engineering, valutazione quantitativa di modelli stocastici e architetture software, con un focus specifico sull'integrazione di queste discipline per risolvere problematiche concrete e ottimizzare applicazioni nel mondo reale.

Leonardo SCOMMEGNA

Curriculum

Scarica il curriculum tramite il seguente link

Leonardo SCOMMEGNA

Pubblicazioni

Legenda

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Tesi di Dottorato

Bertocci N.; Carnevali L.; Scommegna L.; Vicario E. (2025). The Omnibus Java Library: Efficient Synthesis of Optimal Signal Schedules for Multimodal Intersections. In: 20th European Workshop on Computer Performance Engineering, EPEW 2024, ita, 2024, Springer Science and Business Media Deutschland GmbH, vol. 15454, pp. 14-28, ISBN:9783031809316 DOI

Maggi K.; Verdecchia R.; Scommegna L.; Vicario E. (2025). Evolution of code technical debt in microservices architectures. THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 222, pp. 0-0, ISSN:0164-1212 DOI

Verdecchia R.; Maggi K.; Scommegna L.; Vicario E. (2024). Technical Debt in Microservices: A Mixed-Method Case Study. In: Proceedings of the 17th European Conference on Software Architecture, ECSA 2023, tur, 2023, Springer Science and Business Media Deutschland GmbH, vol. 14590, pp. 217-236, ISBN:9783031663253 DOI

Maggi K.; Verdecchia R.; Scommegna L.; Vicario E. (2024). CLAIM: a Lightweight Approach to Identify Microservices in Dockerized Environments. In: 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024, ita, 2024, Association for Computing Machinery, pp. 357-362. DOI

Verdecchia R.; Scommegna L.; Vicario E.; Pecorella T. (2024). Designing a Future-Proof Reference Architecture for Network Digital Twins. In: 17th European Conference on Software Architecture, ECSA 2023, tur, 2023, Springer Science and Business Media Deutschland GmbH, vol. 14590, pp. 287-306, ISBN:9783031663260 DOI

Scommegna L.; Verdecchia R.; Vicario E. (2024). Unveiling Faulty User Sequences: A Model-Based Approach to Test Three-Tier Software Architectures. THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 212, pp. 1-19, ISSN:0164-1212 DOI Accesso ONLINE all'editore

Carnevali, Laura; Paolieri, Marco; Reali, Riccardo; Scommegna, Leonardo; Vicario, Enrico (2024). Cost-Effective Software Rejuvenation Combining Time-Based and Inspection-Based Policies. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, pp. 1-16, ISSN:2168-6750 DOI

Becattini M.; Carnevali L.; Fontani G.; Paroli L.; Scommegna L.; Masoumi M.; De Miguel I.; Brasca F. (2024). Dynamic MEC resource management for URLLC in Industry X.0 scenarios: A quantitative approach based on digital twin networks. In: 7th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2024, ita, 2024, Institute of Electrical and Electronics Engineers Inc., pp. 372-376. DOI

Verdecchia R.; Scommegna L.; Picano B.; Becattini M.; Vicario E. (2024). Network Digital Twins: A Systematic Review. IEEE ACCESS, vol. 12, pp. 145400-145416, ISSN:2169-3536 DOI

Bertocci, Nicola; Carnevali, Laura; Scommegna, Leonardo; Vicario, Enrico (2024). Efficient derivation of optimal signal schedules for multimodal intersections. SIMULATION MODELLING PRACTICE AND THEORY, vol. 133, pp. 1-21, ISSN:1569-190X DOI

Picano, Benedetta; Reali, Riccardo; Scommegna, Leonardo; Vicario, Enrico (2024). Elastic Autoscaling for Distributed Workflows in MEC Networks. In: Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologie, springer, pp. 151-160, ISBN:9783031579301 DOI

Carnevali, Laura; Cerboni, Stefania; Picano, Benedetta; Scommegna, Leonardo; Vicario, Enrico (2024). An observation metamodel for dependability tools. In: 19th European Dependable Computing Conference (EDCC), ieee, pp. 169-172. DOI

Scommegna, Leonardo; Becattini, Marco; Fontani, Giovanni; Paroli, Leonardo; Vicario, Enrico (2024). Quantitative evaluation of software rejuvenation of a pool of service replicas. In: INTERNATIONAL WORKSHOP ON SOFTWARE AGING AND REJUVENATION, IEEE, pp. 402-409. DOI

Botta, Alessio; Canonico, Roberto; Navarro, Annalisa; Stanco, Giovanni; Ventre, Giorgio; Buonocunto, Antonio; Fresa, Antonio; Gentile, Vincenzo; Scommegna, Leonardo; Vicario, Enrico (2024). Edge to Cloud Network Function Offloading in the ADAPTO Framework. In: International Conference on Advanced Information Networking and Applications, Springer, pp. 69-78, ISBN:9783031579301 DOI

Carnevali L.; Paolieri M.; Picano B.; Reali R.; Scommegna L.; Vicario E. (2023). A Quantitative Approach to Coordinated Scaling of Resources in Complex Cloud Computing Workflows. In: EPEW ASMTA 2023, springer, vol. 14231 LNCS, pp. 309-324, ISBN:9783031431845 DOI

Leonardo Scommegna (2023). Taming Runtime Dependencies across Transient Stateful Components in the Business Logic of Software Architectures. (Tutor: Enrico Vicario)

Carnevali L.; Paolieri M.; Reali R.; Scommegna L.; Tammaro F.; Vicario E. (2023). Using the ORIS Tool and the SIRIO Library for Model-Driven Engineering of Quantitative Analytics. In: 18th European Workshop on Computer Performance Engineering, EPEW 2022, esp, 2022, Springer Science and Business Media Deutschland GmbH, vol. 13659, pp. 200-215, ISBN:978-3-031-25048-4 DOI

Picano B, Scommegna L, Vicario E, Fantacci R. (2023). Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks. JOURNAL OF SENSOR AND ACTUATOR NETWORKS, pp. 1-10, ISSN:2224-2708 DOI

Carnevali L.; Paolieri M.; Reali R.; Scommegna L.; Vicario E. (2022). A Markov Regenerative Model of Software Rejuvenation Beyond the Enabling Restriction. In: 33rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022, USA, 2022, Institute of Electrical and Electronics Engineers Inc., pp. 138-145, ISBN:978-1-6654-7679-9 DOI

Parri, Jacopo; Sampietro, Samuele; Scommegna, Leonardo; Vicario, Enrico (2021). Evaluation of software aging in component-based Web Applications subject to soft errors over time. In: INTERNATIONAL WORKSHOP ON SOFTWARE AGING AND REJUVENATION, IEEE COMPUTER SOCiety, pp. 25-32. DOI

Capobianco, Samuele*; Scommegna, Leonardo; Marinai, Simone (2018). Historical handwritten document segmentation by using a weighted loss. In: 8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018, ita, 2018, Springer Verlag, vol. 11081, pp. 395-406, ISBN:9783319999777 DOI Accesso ONLINE all'editore

Leonardo SCOMMEGNA

Biography

Received a Master's degree in Computer Engineering from the University of Florence in 2019 and a PhD in Smart Computing in 2023.

Currently works as a researcher at the Department of Information Engineering at the University of Florence and is a member of the Software Technologies Lab.

Research interests include:
Model-Driven engineering, quantitative evaluation of stochastic models and software architectures, with a specific focus on integrating these disciplines to solve concrete problems and optimize real-world applications.

Leonardo SCOMMEGNA

Curriculum

Download my Curriculum Vitae through the following link