NN-SDCTest at the ICST 2025 Tool Competition - Self-Driving Car Testing Track

Testing self-driving cars (SDCs) requires extensive simulation-based testing, making efficient test case selection important. This paper presents two approaches for test case selection in SDC testing: a curvature-based selector that analyzes road geometry and a graph neural network (GNN) based selector that learns failure patterns. The curvature-based approach uses road geometry analysis, turn detection, and group-based selection strategies, while the GNN approach has a four-layer neural architecture with feature engineering for predicting test failures. Both approaches are implemented and evaluated as part of the ICST 2025 Tool Competition for SDC testing. Our experimental results show that the GNN selector achieves superior computational efficiency (initialization: 1.45s vs 15.27s, selection: 0.30s vs 4.06s) and a better time-to-fault ratio (209.78 vs 239.48), while the curvature-based selector demonstrates stronger fault detection capabilities with a higher fault-to-selection ratio (0.214 vs 0.177). Both approaches maintain comparable diversity scores (0.037 and 0.039 respectively), demonstrating their effectiveness in achieving comprehensive test coverage. The comparative analysis provides insights into the strengths of geometric analysis and machine learning approaches in SDC test selection.