Focused test generation for autonomous driving systems
Informazioni aggiuntive
Autori
Zohdinasab T.,
Riccio V.,
Tonella P.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2024
Lingua
Inglese
Sommario
Testing Autonomous Driving Systems (ADSs) is crucial to ensure their reliability when navigating complex environments. ADSs may exhibit unexpected behaviours when presented, during operation, with driving scenarios containing features inadequately represented in the training dataset. To address this shift from development to operation, developers must acquire new data with the newly observed features. This data can be then utilised to fine tune the ADS, so as to reach the desired level of reliability in performing driving tasks. However, the resource-intensive nature of testing ADSs requires efficient methodologies for generating targeted and diverse tests. In this work, we introduce a novel approach, DeepAtash-LR, that incorporates a surrogate model into the focused test generation process. This integration significantly improves focused testing effectiveness and applicability in resource-intensive scenarios. Experimental results show that the integration of the surrogate model is fundamental to the success of DeepAtash-LR. Our approach was able to generate an average of up to 60× more targeted, failure-inducing inputs compared to the baseline approach. Moreover, the inputs generated by DeepAtash-LR were useful to significantly improve the quality of the original ADS through fine tuning.
Parole chiave
Software and its engineering, Software testing and debugging, Software testing, Deep learning, Search based software engineering, Autonomous driving systems
Periodico
ACM Transactions on Software Engineering and Methodology
Volume
33
Numero ( Mese )
6
Pagine (o numero dell’articolo)
1-32
ISSN
1049-331X, 1557-7392
DOI
Diffusione
Licenza
Diritti riservati
Visibilità
Pubblico
Status open access
Hybrid
