Enhancing autonomous driving simulations: A hybrid metamorphic testing framework with metamorphic relations generated by GPT
- 15 July 2025
- journal article
- Published by Elsevier in Information and Software Technology
- Vol. 187
Abstract
No abstract availableFunding Information
- Australian Research Council (DP210102447)
- University of Nottingham
This publication has 24 references indexed in Scilit:
- MRGS‐ART: Metamorphic Relation and Group Selection Based on Adaptive Random TestingSoftware Testing, Verification and Reliability, 2024
- A comparison of human, GPT-3.5, and GPT-4 performance in a university-level coding courseScientific Reports, 2024
- Scenario‐Driven Metamorphic Testing for Autonomous Driving SimulatorsSoftware Testing, Verification and Reliability, 2024
- SFIDMT-ART: A metamorphic group generation method based on Adaptive Random Testing applied to source and follow-up input domainsInformation and Software Technology, 2024
- Metamorphic testing of Advanced Driver-Assistance System (ADAS) simulation platforms: Lane Keeping Assist System (LKAS) case studiesInformation and Software Technology, 2022
- How to keep it adequate: A protocol for ensuring validity in agent-based simulationEnvironmental Modelling & Software, 2022
- Self-driving cars: A surveyExpert Systems with Applications, 2021
- Systematic review of driving simulator validation studiesSafety Science, 2019
- A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directionsNeuroImage, 2012
- Effect of load, processing time and due date variation on the effectiveness of scheduling rulesInternational Journal of Production Research, 2002