DRVN at the ICST 2025 Tool Competition – Self-Driving Car Testing Track
A.J. Bartlett (TU Delft - Multimedia Computing)
C.C.S. Liem (TU Delft - Multimedia Computing)
A. Panichella (TU Delft - Software Engineering)
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Abstract
DRVN is a regression testing tool that aims to diversify the test scenarios (road maps) to execute for testing and validating self-driving cars. DRVN harnesses the power of convolutional neural networks to identify possible failing roads in a set of generated examples before applying a greedy algorithm that selects and prioritizes the most diverse roads during regression testing. Initial testing discovered that DRVN performed well against random-based test selection.