  Large Language Models (LLMs) have been widely employed in programming
language analysis to enhance human productivity. Yet, their reliability can be
compromised by various code distribution shifts, leading to inconsistent
outputs. While probabilistic methods are known to mitigate such impact through
uncertainty calibration and estimation, their efficacy in the language domain
remains underexplored compared to their application in image-based tasks. In
this work, we first introduce a large-scale benchmark dataset, incorporating
three realistic patterns of code distribution shifts at varying intensities.
Then we thoroughly investigate state-of-the-art probabilistic methods applied
to CodeLlama using these shifted code snippets. We observe that these methods
generally improve the uncertainty awareness of CodeLlama, with increased
calibration quality and higher uncertainty estimation~(UE) precision. However,
our study further reveals varied performance dynamics across different criteria
(e.g., calibration error vs misclassification detection) and trade-off between
efficacy and efficiency, highlighting necessary methodological selection
tailored to specific contexts.
