  Code editing encompasses a variety of pragmatic tasks that developers deal
with daily. Despite its relevance and practical usefulness, automatic code
editing remains an underexplored area in the evolution of deep learning models,
partly due to data scarcity. In this work, we explore the use of Large Language
Models (LLMs) to edit code based on user instructions. Evaluated on a novel
human-written execution-based benchmark dubbed EditEval, we found current
models often struggle to fulfill the instructions. In light of this, we
contribute InstructCoder, the first instruction-tuning dataset designed to
adapt LLMs for general-purpose code editing, containing high-diversity
code-editing tasks such as comment insertion, code optimization, and code
refactoring. It consists of over 114,000 instruction-input-output triplets and
covers multiple distinct code editing scenarios. The collection process starts
with filtered commit data sourced from GitHub Python repositories as seeds.
Subsequently, the dataset is systematically expanded through an iterative
process, where both seed and generated tasks are used to prompt ChatGPT for
more data. Our findings reveal that open-source LLMs fine-tuned on
InstructCoder can significantly enhance the accuracy of code edits, exhibiting
superior code-editing performance matching advanced proprietary LLMs. The
datasets and the source code are publicly available at
https://github.com/qishenghu/CodeInstruct.
