1 citations · 1 across the 3 of their papers we have counts for
6 papers
Search-based Testing of Vision Language Models for In-Car Scene Understanding
Lev Sorokin, Chen Yang, Ken E. Friedl +1
In the automotive domain, in-car scene understanding (ISU) enables the detection of safety-critical events, such as driver distraction, and supports drivers or passengers by analyz…
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…
DeepTest Tool Competition 2026: Benchmarking an LLM-Based Automotive Assistant
Lev Sorokin, Ivan Vasilev, Samuele Pasini
This report summarizes the results of the first edition of the Large Language Model (LLM) Testing competition, held as part of the DeepTest workshop at ICSE 2026. Four tools compet…
STELLAR: A Search-Based Testing Framework for Large Language Model Applications
Lev Sorokin, Ivan Vasilev, Ken E. Friedl +1
Large Language Model (LLM)-based applications are increasingly deployed across various domains, including customer service, education, and mobility. However, these systems are pron…
Benchmarking Contextual Understanding for In-Car Conversational Systems
Philipp Habicht, Lev Sorokin, Abdullah Saydemir +2
In-Car Conversational Question Answering (ConvQA) systems significantly enhance user experience by enabling seamless voice interactions. However, assessing their accuracy and relia…
Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models
Rafael Giebisch, Ken E. Friedl, Lev Sorokin +1
In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them…