  Generative deep learning (DL) models have been successfully adopted for
vulnerability patching. However, such models require the availability of a
large dataset of patches to learn from. To overcome this issue, researchers
have proposed to start from models pre-trained with general knowledge, either
on the programming language or on similar tasks such as bug fixing. Despite the
efforts in the area of automated vulnerability patching, there is a lack of
systematic studies on how these different training procedures impact the
performance of DL models for such a task. This paper provides a manyfold
contribution to bridge this gap, by (i) comparing existing solutions of
self-supervised and supervised pre-training for vulnerability patching; and
(ii) for the first time, experimenting with different kinds of prompt-tuning
for this task. The study required to train/test 23 DL models. We found that a
supervised pre-training focused on bug-fixing, while expensive in terms of data
collection, substantially improves DL-based vulnerability patching. When
applying prompt-tuning on top of this supervised pre-trained model, there is no
significant gain in performance. Instead, prompt-tuning is an effective and
cheap solution to substantially boost the performance of self-supervised
pre-trained models, i.e., those not relying on the bug-fixing pre-training.
