Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining objective which explicitly models edits and use it to build CoditT5, a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments. We fine-tune it on various downstream editing tasks, including comment updating, bug fixing, and automated code review. By outperforming standard generation-based models, we demonstrate the generalizability of our approach and its suitability for editing tasks. We also show how a standard generation model and our edit-based model can complement one another through simple reranking strategies, with which we achieve state-of-the-art performance for the three downstream editing tasks.
@article{arxiv.2208.05446,
title = {CoditT5: Pretraining for Source Code and Natural Language Editing},
author = {Jiyang Zhang and Sheena Panthaplackel and Pengyu Nie and Junyi Jessy Li and Milos Gligoric},
journal= {arXiv preprint arXiv:2208.05446},
year = {2022}
}