works on

From the 1 of 10 linked papers with an AI index.

activity
20242026
most citedSimulator Ensembles for Trustworthy Autonomous Driving Systems Testing

1 citations · 1 across the 5 of their papers we have counts for

collaborators

10 papers

cs.SE2026

Explanation-Guided Metamorphic Testing of Specialized Language Models: An Empirical Study

Xingcheng Chen, Mehmet Besenk, Andrea Stocco

\head{Background} Task-specialized language models are increasingly integrated into software engineering workflows to support vertical-domain activities such as issue triaging, doc…

cs.LG2026

HyperNet-Adaptation for Diffusion-Based Test Case Generation

Oliver Weißl, Vincenzo Riccio, Severin Kacianka +1

HyNeA introduces hypernetwork-based control of diffusion models to efficiently generate realistic, failure-inducing test cases without needing labeled datasets, reducing computatio…

cs.CR2026

Generative Testing of Automated Speech Recognition Systems

Yanis Xabier Wilbrand Peña, Oliver Weißl, Andrea Stocco

Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications. However, they remain vulnerable…

cs.SE20261 cited

Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing

Lev Sorokin, Matteo Biagiola, Andrea Stocco

Scenario-based testing with driving simulators is extensively used to identify failing conditions of automated driving assistance systems (ADAS). However, existing studies have sho…

cs.SE2026

E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios

Manfredi Napolitano, Alessandra Somma, Alessio Gambi +2

Autonomous driving research has largely focused on safety while giving limited attention to non-functional aspects such as energy consumption and sustainability. As Autonomous Elec…

cs.SE2026

Latent Regularization in Generative Test Input Generation

Giorgi Merabishvili, Oliver Weißl, Andrea Stocco

This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning classifiers. We evaluate this eff…