Paolo Arcaini

Paolo Arcaini is a Project Associate Professor at the National Institute of Informatics (2019–present) and previously held academic positions at Charles University (Assistant Professor, 2015–2018) and the University of Bergamo (Postdoctoral Fellow). His research spans formal methods, model-based testing, and quantum software engineering, with a focus on cyber-physical systems, autonomous vehicles, and machine learning validation. He has received multiple best paper awards, including at SSBSE (2021), AITest (2020), and Mutation (2017).

Educational Background:

• PhD in Computer Science, University of Milan (2010–2012, advisor: Elvinia Riccobene)
• Postdoctoral Fellow, CNR-IDPA (2013, advisor: Gloria Bordogna)

Research Interests: Dr. Arcaini's work bridges abstract state machine modeling, hybrid system falsification, and quantum program testing. His recent projects address robustness in neural networks, search-based scenario generation for autonomous systems, and quantum annealing applications to test case optimization.

Article Trends: His publications demonstrate expertise in

• hybrid system falsification using Monte Carlo tree search
• mutation analysis for regular expressions and neural networks
• quantum software testing via combinatorial and search-based methods
• tool development for NuSMV/Yices integration and quantum noise mitigation
• multi-objective optimization for autonomous delivery systems
• formal methods in medical software validation

Scientific Recognition:

• Distinguished Paper Award, SSBSE 2021
• Best Paper Award, AITest 2020
• Best Paper Award, Mutation 2017

Teaching: He has taught courses on

• Formal Methods for Cyber-Physical Systems (NII 2019)
• Model-Based Testing (Charles University 2015–2018)
• Java programming labs and advanced topics

Tool Development: Dr. Arcaini has created several tools including

• MutRex/TearRex for regular expression testing
• NuSeen (Eclipse-based NuSMV environment)
• ATGT Boolean for SAT-based testing
• CRAG for combinatorial road geometry generation
• QUIET for quantum noise mitigation
• BQTmizer for quantum test case optimization