arXiv subjects

Oliver Weißl

Publications and source records attributed to Oliver Weißl.

5 recordsLinked to original sources

arXiv2026

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

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 translation. In this work, we investigate the effectiveness of LLMs in code translation and translation error repair. First, we present CodeTransBenchmark, a framework for evaluating LLM-based translation and repair and devise a post-processing strategy to extract code from inconsistent LLM outputs. Then, we discuss an empirical study evaluates eight models on three datasets and 12 language pairs, in which we categorize incorrect translations by errors to identify weaknesses of existing LLMs. Our work shows that while LLMs specifically trained for multi-lingual coding, like Codestral, correctly translate the majority of code, most general-purpose models struggle with the syntactic rules of the target language. The analysis of erroneous translations reveals the substantial impact of the interrelationship between involved programming languages and training data on the effectiveness. We show that a general post-processing approach must tolerate inconsistencies and leverage the predictability of LLM answers. Further, we show that iterative translation repair via automated feedback significantly improves translation accuracy. While our combined findings highlight the potential of LLMs to automate code translation, an effective deployment of LLM-based code translation in practice would require models with larger context windows.

cs.SE↗

arXiv2026

HyperNet-Adaptation for Diffusion-Based Test Case Generation

The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios. Traditional gradient-based adversarial attacks introduce small perturbations that rarely correspond to realistic failures and mainly assess robustness rather than functional behavior. Generative test generation methods offer an alternative but are often limited to simple datasets or constrained input domains. Although diffusion models enable high-fidelity image synthesis, their computational cost and limited controllability restrict their applicability to large-scale testing. We present HyNeA, a generative testing method that enables direct and efficient control over diffusion-based generation. HyNeA provides dataset-free controllability through hypernetworks, allowing targeted manipulation of the generative process without relying on architecture-specific conditioning mechanisms or dataset-driven adaptations such as fine-tuning. HyNeA employs a distinct training strategy that supports instance-level tuning to identify failure-inducing test cases without requiring datasets that explicitly contain examples of similar failures. This approach enables the targeted generation of realistic failure cases at substantially lower computational cost than search-based methods. Experimental results show that HyNeA improves controllability and test diversity compared to existing generative test generators and generalizes to domains where failure-labeled training data is unavailable.

cs.LG↗

arXiv2026

Generative Testing of Automated Speech Recognition Systems

Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications. However, they remain vulnerable to adversarial manipulation, particularly in black-box settings where attacks must preserve perceptual naturalness. This work introduces GATAS, a black-box testing approach that generates failure inducing inputs by operating in the phoneme-level latent space of a text- to-speech model. Instead of perturbing waveforms directly, the approach interpolates latent representations to induce transcription errors while remaining within the manifold of natural speech. The attack is formulated as a multi-objective optimization problem balancing semantic divergence and perceptual quality. Our empirical evaluation against both white-box and black-box baselines shows that GATAS achieves a 98% success rate while producing lower distortion and higher perceptual quality, as confirmed by human studies. Despite operating without gradient access, GATAS remains competitive against white-box methods, highlighting that representation and perceptual alignment are more critical than access to model internals. Overall, our results demonstrate that untargeted latent-space optimization enables the efficient generation of realistic and effective test cases for ASR systems.

cs.CR↗

arXiv2026

Latent Regularization in Generative Test Input Generation

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 effect using style-based GANs, a state-of-the-art generative approach, and assess quality along three dimensions: validity, diversity, and fault detection. We evaluate our approach on the boundary testing of deep learning image classifiers across three datasets, MNIST, Fashion MNIST, and CIFAR-10. We compare two truncation strategies: latent code mixing with binary search optimization and random latent truncation for generative exploration. Our experiments show that the latent code-mixing approach yields a higher fault detection rate than random truncation, while also improving both diversity and validity.

cs.SE↗

arXiv2025

Targeted Deep Learning System Boundary Testing

Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted random, model- or latent-based perturbations, due to difficulties in generating controlled input variations. In this work, we introduce Mimicry, a novel black-box test generator for fine-grained, targeted exploration of DL system boundaries. Mimicry performs boundary testing by leveraging the probabilistic nature of DL outputs to identify promising directions for exploration. It uses style-based GANs to disentangle input representations into content and style components, enabling controlled feature mixing to approximate the decision boundary. We evaluated Mimicry's effectiveness in generating boundary inputs for five widely used DL image classification systems of increasing complexity, comparing it to two baseline approaches. Our results show that Mimicry consistently identifies inputs closer to the decision boundary. It generates semantically meaningful boundary test cases that reveal new functional (mis)behaviors, while the baselines produce mainly corrupted or invalid inputs. Thanks to its enhanced control over latent space manipulations, Mimicry remains effective as dataset complexity increases, maintaining competitive diversity and higher validity rates, confirmed by human assessors.

cs.SE↗