activity
20212026
most citedArchitecture-Guided Test Resource Allocation Via Logic

1 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

Software Testing in the Quantum World

Rui Abreu, Shaukat Ali, Paolo Arcaini +11

Quantum computing offers significant speedups for simulating physical, chemical, and biological systems, and for optimization and machine learning. As quantum software grows in com…

cs.SE2022★ 1 cited

Does Road Diversity Really Matter in Testing Automated Driving Systems? -- A Registered Report

Stefan Klikovits, Vincenzo Riccio, Ezequiel Castellano +3

Background/Context. The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large se…

cs.LG2022

Dynamic Shielding for Reinforcement Learning in Black-Box Environments

Masaki Waga, Ezequiel Castellano, Sasinee Pruekprasert +3

It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to r…

cs.RO2021

Handling Noise in Search-Based Scenario Generation for Autonomous Driving Systems

Stefan Klikovits, Paolo Arcaini

This paper presents the first evaluation of k-nearest neighbours-Averaging (kNN-Avg) on a real-world case study. kNN-Avg is a novel technique that tackles the challenges of noisy m…

cs.SE2021

On the Need for Multi-Level ADS Scenarios

Stefan Klikovits, Paolo Arcaini

Currently, most existing approaches for the design of Automated Driving System (ADS) scenarios focus on the description at one particular abstraction level typically the most detai…

cs.NE2021★ 1 cited

KNN-Averaging for Noisy Multi-objective Optimisation

Stefan Klikovits, Paolo Arcaini

Multi-objective optimisation is a popular approach for finding solutions to complex problems with large search spaces that reliably yields good optimisation results. However, with…

cs.SE2021★ 1 cited

Architecture-Guided Test Resource Allocation Via Logic

Clovis Eberhart, Akihisa Yamada, Stefan Klikovits +4

We introduce a new logic named Quantitative Confidence Logic (QCL) that quantifies the level of confidence one has in the conclusion of a proof. By translating a fault tree represe…