Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation

• URL: http://arxiv.org/abs/2411.10129v1
• Date: Fri, 15 Nov 2024 12:01:38 GMT
• Title: Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation
• Authors: Md. Asif Haider, Ayesha Binte Mostofa, Sk. Sabit Bin Mosaddek, Anindya Iqbal, Toufique Ahmed,
• Abstract summary: Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. We fine-tune open-source Large language models (LLM) in parameter-efficient, quantized low-rank fashion on consumer-grade hardware to improve review comment generation.
• Score: 5.6001617185032595
• License:
• Abstract: Generating accurate code review comments remains a significant challenge due to the inherently diverse and non-unique nature of the task output. Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. However, large-scale pretraining is not always feasible due to its environmental impact and project-specific generalizability issues. In this work, first we fine-tune open-source Large language models (LLM) in parameter-efficient, quantized low-rank (QLoRA) fashion on consumer-grade hardware to improve review comment generation. Recent studies demonstrate the efficacy of augmenting semantic metadata information into prompts to boost performance in other code-related tasks. To explore this in code review activities, we also prompt proprietary, closed-source LLMs augmenting the input code patch with function call graphs and code summaries. Both of our strategies improve the review comment generation performance, with function call graph augmented few-shot prompting on the GPT-3.5 model surpassing the pretrained baseline by around 90% BLEU-4 score on the CodeReviewer dataset. Moreover, few-shot prompted Gemini-1.0 Pro, QLoRA fine-tuned Code Llama and Llama 3.1 models achieve competitive results (ranging from 25% to 83% performance improvement) on this task. An additional human evaluation study further validates our experimental findings, reflecting real-world developers' perceptions of LLM-generated code review comments based on relevant qualitative metrics.

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