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DeFT: Maintaining Determinism and Extracting Unit Tests for Autonomous Driving Planning

integration_instructionsResearch softwarekeyboard_double_arrow_right Software Under curationPublisher:ZenodoFunded by:NSF
Authors: Huai, Yuqi; Chen, Yuntianyi; Wan, Ziwen; Chen, Qi; Garcia, Joshua;

doi: 10.5281/zenodo.21985800

DeFT: Maintaining Determinism and Extracting Unit Tests for Autonomous Driving Planning

Abstract

This repository corresponds to the ICSE 2026 Research Track paper and its accompanying artifact, DeFT, a tool and methodology designed to improve testing reliability in autonomous driving systems by addressing non-determinism in planning tests. Traditional system-level scenario tests often produce varying outcomes, making failure reproduction and debugging challenging. DeFT is a methodology that converts non-deterministic system-level scenario tests into deterministic module-level tests by extracting and reconstructing module inputs.

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