A Transformer-based Model for Assisting Dockerfile Revising
Yiwen Wu, Yang Zhang, Tao Wang, Huaimin Wang · 2024
Dockerfile plays an important role in the containerized software development process since it specifies the structure and functionality of the built Docker image. Currently, Dockerfile writing and modification still rely on manual operations which can be time-consuming. Thus, there is a need for automation tools to support the Dockerfile revising process. In this study, we focus on utilizing pre-training techniques for the tasks in the Dockerfile revising scenario. We propose a Transformer-based model and pre-train it with an instruction-aware objective. Furthermore, we fine-tune our model in two downstream tasks, including revision opportunity estimation and revision activity prediction. The experimental results show that our model outperforms the baseline models.