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The automation of code review activities, a long-standing pursuit in softwareengineering, has been primarily addressed by numerous domain-specificpre-trained models. Despite their success, these models frequently demandextensive resources for pre-training from scratch. In contrast, Large LanguageModels (LLMs) provide an intriguing alternative, given their remarkablecapabilities when supplemented with domain-specific knowledge. However, theirpotential for automating code review tasks remains largely unexplored.  In response to this research gap, we present LLaMA-Reviewer, an innovativeframework that leverages the capabilities of LLaMA, a popular LLM, in the realmof code review. Mindful of resource constraints, this framework employsparameter-efficient fine-tuning (PEFT) methods, delivering high performancewhile using less than 1% of trainable parameters.  An extensive evaluation of LLaMA-Reviewer is conducted on two diverse,publicly available datasets. Notably, even with the smallest LLaMA base modelconsisting of 6.7B parameters and a limited number of tuning epochs,LLaMA-Reviewer equals the performance of existing code-review-focused models.  The ablation experiments provide insights into the influence of variousfine-tuning process components, including input representation, instructiontuning, and different PEFT methods. To foster continuous progress in thisfield, the code and all PEFT-weight plugins have been made open-source.

LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning