تقرير
How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
| العنوان: | How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching |
|---|---|
| المؤلفون: | Mastropaolo, Antonio, Nardone, Vittoria, Bavota, Gabriele, Di Penta, Massimiliano |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Software Engineering |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2404.17896Test |
| رقم الانضمام: | edsarx.2404.17896 |
| قاعدة البيانات: | arXiv |
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