most citedTargeted Deep Learning System Boundary Testing

4 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

CodeTransBenchmark: Evaluating LLM-based Code Translation and Repair Across Programming Languages

Vera Kowalczuk, Oliver Weißl, Severin Kacianka +1

Large Language Models (LLMs) pre-trained on expansive text and code corpora have revealed promising code generation abilities and have attracted increasing attention in code transl…

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.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…

cs.SE2026

Feature-Aware Test Generation for Deep Learning Models

Xingcheng Chen, Oliver Weissl, Andrea Stocco

As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deployment. To date, the most advan…

cs.LG2026

HyperNet-Adaptation for Diffusion-Based Test Case Generation

Oliver Weißl, Vincenzo Riccio, Severin Kacianka +1

The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios. Traditional gradient-based adversarial attacks intro…

quant-ph2024

Equivariant Machine Learning Decoder for 3D Toric Codes

Oliver Weissl, Evgenii Egorov

Mitigating errors in computing and communication systems has seen a great deal of research since the beginning of the widespread use of these technologies. However, as we develop n…

cs.SE2024★ 4 cited

Targeted Deep Learning System Boundary Testing

Oliver Weißl, Amr Abdellatif, Xingcheng Chen +4

Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as th…