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Tonella_2024_ACM_3664605
Journal article

Focused test generation for autonomous driving systems

  • 2024
Published in:
  • ACM Transactions on Software Engineering and Methodology. - 2024, vol. 33, no. 6, p. 1-32
Software and its engineering
Software testing and debugging
Software testing
Deep learning
Search based software engineering
Autonomous driving systems
English 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.
Collections
Language
  • English
Classification
Computer science and technology
License
Rights reserved
Open access status
hybrid
Identifiers
  • ISSN 1049-331X
  • ISSN 1557-7392
  • DOI 10.1145/3664605
  • RICERCO 37661
  • ARK ark:/12658/srd1333986
Persistent URL
https://n2t.net/ark:/12658/srd1333986
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