On Calibration of Pre-trained Code Models

Abstract

Pre-trained code models have achieved notable success in the field of Software Engineering (SE). However, existing studies have predominantly focused on improving model performance, with limited attention given to other critical aspects such as model calibration. Model calibration, which refers to the accurate estimation of predictive uncertainty, is a vital consideration in practical applications. Therefore, in order to advance the understanding of model calibration in SE, we conduct a comprehensive investigation into the calibration of pre-trained code models in this paper. Our investigation focuses on five pre-trained code models and four code understanding tasks, including analyses of calibration in both in-distribution and out-of-distribution settings. Several key insights are uncovered: (1) pre-trained code models may suffer from the issue of over-confidence; (2) temperature scaling and label smoothing are effective in calibrating code models in in-distribution data; (3) the issue of over-confidence in pre-trained code models worsens in different out-of-distribution settings, and the effectiveness of temperature scaling and label smoothing diminishes. All materials used in our experiments are available at https://github.com/queserasera22/Calibration-of-Pretrained-Code-Models.