Investigating the Effectiveness of Mutation Testing Tools in the Context of Deep Neural Networks. - Nour Chetouane; Lorenz Klampfl; Franz Wotawa (IWANN (1), 2019)

Explaining Object Motion Using Answer Set Programming. - Franz Wotawa; Lorenz Klampfl (ISMIS, 2020)

Mutation Testing for Artificial Neural Networks: An Empirical Evaluation. - Lorenz Klampfl; Nour Chetouane; Franz Wotawa (QRS, 2020)

Critical and Challenging Scenario Generation based on Automatic Action Behavior Sequence Optimization: 2021 IEEE Autonomous Driving AI Test Challenge Group 108. - David Kaufmann; Lorenz Klampfl; Florian Klück; Martin Zimmermann 007; Jianbo Tao (AITest, 2021)

A framework for the automation of testing computer vision systems. - Franz Wotawa; Lorenz Klampfl; Ledio Jahaj (AST@ICSE, 2021)

GABezier at the SBST 2021 Tool Competition. - Florian Klück; Lorenz Klampfl; Franz Wotawa (SBST@ICSE, 2021)

Extracting information from driving data using k-means clustering (S). - Nour Chetouane; Lorenz Klampfl; Franz Wotawa (SEKE, 2021)

Automatic Generation of Challenging Road Networks for ALKS Testing based on Bezier Curves and Search. - Florian Klück; Lorenz Klampfl; Franz Wotawa (CoRR, 2021)

Identifying Critical Scenarios in Autonomous Driving During Operation. - Lorenz Klampfl; Franz Wotawa (ECAI Workshops (1), 2023)

Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems. - Lorenz Klampfl; Franz Wotawa (DX, 2024)

Knowledge-Based Monitoring for Checking Law and Regulation Compliance. - Ledio Jahaj; Lorenz Klampfl; Franz Wotawa (IEA/AIE, 2024)

Using genetic algorithms for automating automated lane-keeping system testing. - Lorenz Klampfl; Florian Klück; Franz Wotawa (J. Softw. Evol. Process., 2024)

Testing ADAS/ADS - from critical scenarios to automated testing oracles. - Lorenz Klampfl; Florian Klück; Mihai Nica; Jianbo Tao; Franz Wotawa (Elektrotech. Informationstechnik, 2024)