Paolo Arcaini

Project Associate Professor

National Institute of Informatics

Japan

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Paolo Arcaini is a Project Associate Professor at the National Institute of Informatics in Japan. His research areas encompass fault localization in AI-enabled cyber-physical systems, quantum software engineering, and the application of quantum neural networks in medical testing. Recent publications include studies on automated program repair and safety assessment in autonomous driving systems, reflecting his focus on the intersection of artificial intelligence and software reliability.

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2020

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2020

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2020

Collaborators (8)

Stefan Klikovits

Lecturer

IMC University of Applied Sciences Krems

AUSTRIA

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Atif Mashkoor

Johannes Kepler University Linz

AUSTRIA

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Juan M. Murillo

SPAIN

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Jianjun Zhao

Kyushu University

JAPAN

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Anthony Ventresque

Associate Prof

Trinity College Dublin

IRELAND

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Angelo Gargantini

University of Bergamo

ITALY

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Ichiro Hasuo

Associate Professor

National Institute of Informatics

JAPAN

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Deyun Lyu

National Institute of Informatics

JAPAN

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Paolo Arcaini

Research Interests

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About

Articles (24)

Fault localization of AI-enabled cyber–physical systems by exploiting temporal neuron activation

Automated Generation of Benchmarks for Falsification of STL Specifications

SpectAcle : Fault Localisation of AI-Enabled CPS by Exploiting Sequences of DNN Controller Inferences

Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks

BQTmizer: A Tool for Test Case Minimization With Quantum Annealing

Quantum Software Engineering: Roadmap and Challenges Ahead

Simulation-based Safety Assessment of Vehicle Characteristics Variations in Autonomous Driving Systems

CRAG – a combinatorial testing-based generator of road geometries for ADS testing

Automated program repair for variability bugs in software product line systems

Test Case Minimization with Quantum Annealers

MET-MAPF: A Metamorphic Testing Approach for Multi-Agent Path Finding Algorithms

Parameter Coverage for Testing of Autonomous Driving Systems under Uncertainty

A Robustness-Based Confidence Measure for Hybrid System Falsification

FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria

Frenetic-lib: An extensible framework for search-based generation of road structures for ADS testing[Formula presented]

Trust your neighbours: Handling noise in multi-objective optimisation using kNN-averaging[Formula presented]

Online Reset for Signal Temporal Logic Monitoring

Mutation-based analysis of queueing network performance models

An Incremental Approach for Understanding Collision Avoidance of an Industrial Path Planner

Intelligent Autonomous Systems

Automated model-based performance analysis of software product lines under uncertainty

Automated model-based performance analysis of software product lines under uncertainty

Hybrid System Falsification Under (In)equality Constraints via Search Space Transformation

Collaborators (8)

Stefan Klikovits

Atif Mashkoor

Juan M. Murillo

Jianjun Zhao

Anthony Ventresque

Angelo Gargantini

Ichiro Hasuo

Deyun Lyu

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