An Empirical Study on Software Defect Prediction Using CodeBERT Model

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

Automated summary

In this research, various CodeBERT models are proposed for software defect prediction, aiming to investigate the potential performance improvement of using a neural language model like CodeBERT in cross-version and cross-project defect prediction. Different prediction patterns in software defect prediction using CodeBERT models are also analyzed in the empirical studies, with further discussion on the results.