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This paper presents an extended version of Deeper, a search-basedsimulation-integrated test solution that generates failure-revealing testscenarios for testing a deep neural network-based lane-keeping system. In thenewly proposed version, we utilize a new set of bio-inspired search algorithms,genetic algorithm (GA), $({\mu}+{\lambda})$ and $({\mu},{\lambda})$ evolutionstrategies (ES), and particle swarm optimization (PSO), that leverage a qualitypopulation seed and domain-specific cross-over and mutation operations tailoredfor the presentation model used for modeling the test scenarios. In order todemonstrate the capabilities of the new test generators within Deeper, we carryout an empirical evaluation and comparison with regard to the results of fiveparticipating tools in the cyber-physical systems testing competition at SBST2021. Our evaluation shows the newly proposed test generators in Deeper notonly represent a considerable improvement on the previous version but alsoprove to be effective and efficient in provoking a considerable number ofdiverse failure-revealing test scenarios for testing an ML-driven lane-keepingsystem. They can trigger several failures while promoting test scenariodiversity, under a limited test time budget, high target failure severity, andstrict speed limit constraints.

Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing