Before revision:  Our results show that CuBERT outperforms the baseline methods by a margin of 2.9-22\%. We also show its superiority when fine-tuned with smaller datasets, and over fewer epochs. We further evaluate CuBERT's effectiveness on a joint classification, localization and repair task involving prediction of two pointers .
After revision:  Our results show that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples. Future work on source-code embedding can benefit from reusing our benchmark, and comparing against CuBERT models as a strong baseline .