Self-adaptive robots adjust their behaviors in response to unpredictable environmental changes. These robots often incorporate deep learning (DL) components into their software to support functionality such as perception, decision-making, and control, enhancing autonomy and self-adaptability. However, the inherent uncertainty of DL-enabled software makes it challenging to ensure its dependability in dynamic environments. Consequently, test generation techniques have been developed to test robot software, and classical mutation analysis injects faults into the software to assess the test suite's effectiveness in detecting the resulting failures. However, there is a lack of mutation analysis techniques to assess the effectiveness under the uncertainty inherent to DL-enabled software. To this end, we propose UAMTERS, an uncertainty-aware mutation analysis framework that introduces uncertainty-aware mutation operators to explicitly inject stochastic uncertainty into DL-enabled robotic software, simulating uncertainty in its behavior. We further propose mutation score metrics to quantify a test suite's ability to detect failures under varying levels of uncertainty. We evaluate UAMTERS across three robotic case studies, demonstrating that UAMTERS more effectively distinguishes test suite quality and captures uncertainty-induced failures in DL-enabled software.

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