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.

大规模语言模型在编程语言分析中被广泛应用以提高人类生产力。本研究通过引入大规模基准数据集，调查了应用概率方法来处理与代码分布变化相关的问题，结果表明这些方法能够提高 CodeLlama 的不确定性感知能力，并增加校准质量和不确定性估计精度。然而，在不同标准（如校准误差与错误分类检测）和效能与效率之间存在不同的性能动态，强调了需根据特定环境进行方法选择的必要性。