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Developers often dedicate significant time to maintaining and refactoringexisting code. However, most prior work on generative models for code focusessolely on creating new code, neglecting the unique requirements of editingexisting code. In this work, we explore a multi-round code auto-editingsetting, aiming to predict edits to a code region based on recent changeswithin the same codebase. Our model, Coeditor, is a fine-tuned CodeT5 modelwith enhancements specifically designed for code editing tasks. We encode codechanges using a line diff format and employ static analysis to form largecustomized model contexts, ensuring appropriate information for prediction. Wecollect a code editing dataset from the commit histories of 1650 open-sourcePython projects for training and evaluation. In a simplified single-round,single-edit task, Coeditor significantly outperforms the best code completionapproach -- nearly doubling its exact-match accuracy, despite using a muchsmaller model -- demonstrating the benefits of incorporating editing historyfor code completion. In a multi-round, multi-edit setting, we observesubstantial gains by iteratively prompting the model with additional useredits. We open-source our code, data, and model weights to encourage futureresearch and release a VSCode extension powered by our model for interactiveusage.

Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing