4 citations · 7 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…
XMutant: XAI-based Fuzzing for Deep Learning Systems
Xingcheng Chen, Matteo Biagiola, Vincenzo Riccio +2
Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random…
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…