CoditT5: Pretraining for Source Code and Natural Language Editing

Autor:	Zhang, Jiyang, Panthaplackel, Sheena, Nie, Pengyu, Li, Junyi Jessy, Gligoric, Milos
Rok vydání:	2022
Předmět:	Computer Science - Software Engineering Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ASE 2022 (camera ready)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.05446 Zobrazit plný text záznamu View this record from Arxiv