What is it about?

Our paper describes an approach to prioritize tests for self-driving cars performed in a virtual environment. The problem of finding an optimal test order is translated into an optimization problem. The optimization problem is solved by applying an evolutionary genetic algorithm. The optimal test order solved by the algorithm makes it possible to detect faults in the testing process as early as possible, but it also ensures a diverse set of tests.

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Why is it important?

System-level testing of self-driving cars is costly and requires a lot of time. We can detect faults earlier by applying an optimization algorithm, and the testing is, therefore, more effective.

Read the Original

This page is a summary of: Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments, ACM Transactions on Software Engineering and Methodology, May 2022, ACM (Association for Computing Machinery), DOI: 10.1145/3533818.
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