Metamorphic Testing of an Automated Parking System: An Experience Report
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Metamorphic Testing of an Automated Parking System: An Experience Report
Dave Towey,
Zepei Luo,
Ziqi Zheng,
Peijian Zhou,
Junbo Yang,
Puttipatt Ingkasit,
Changyang Lao,
Matthew Pike,
Yifan Zhang
COMPSAC 2023 - 47th Annual Computers, Software, and Applications Conference | 2023
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Abstract
Automated Driving Systems (ADSs) have gained popularity, but their reliability and safety remain key concerns due to the test oracle problem. This paper reports on the application of Metamorphic Testing (MT) to evaluate an Automated Parking System (APS) in Baidu Apollo, an open-source ADS platform. The study integrates Mutation Analysis (MA) to assess the sufficiency of MT-based testing. The experience highlights how MT can alleviate oracle-related challenges by defining Metamorphic Relations (MRs) to validate APS behavior. The study also explores the creation of Open Educational Resources (OERs) for disseminating knowledge on MT in ADS testing. The results demonstrate the effectiveness of MT in identifying potential issues in APS and underscore the value of MA in refining MR-based testing strategies. The paper concludes with recommendations for future improvements in MT applications for ADS evaluation.