FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

2025-12-28 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, short

Summary

FasterPy is a novel, low-cost framework designed to optimize the execution efficiency of Python code using Large Language Models (LLMs). Proposed in a paper submitted on 28 Dec 2025 and revised on 15 Jun 2026, FasterPy addresses the limitations of traditional rule-based and deep learning methods for performance bug optimization, which are often labor-intensive or costly to scale. The framework integrates Retrieval-Augmented Generation (RAG), leveraging a knowledge base of performance-improving code pairs and measurements, with Low-Rank Adaptation (LoRA) to enhance its code optimization capabilities. Experimental results on the Performance Improving Code Edits (PIE) benchmark demonstrate that FasterPy outperforms existing models across multiple metrics. The associated tool and experimental data are publicly available, detailed in a 38-page paper with 5 images and 14 tables.

Key takeaway

For Python developers or ML engineers focused on optimizing code performance, FasterPy presents a compelling LLM-based approach. You should consider evaluating this framework, especially if traditional rule-based or deep learning methods prove too costly or limited for your specific performance bug challenges. Its combination of RAG and LoRA offers a potentially more efficient and scalable solution for improving Python execution speed.

Key insights

FasterPy optimizes Python code execution efficiency by combining LLMs with RAG and LoRA, outperforming existing methods.

Principles

• LLMs can be adapted for code optimization.
• RAG enhances LLM performance with domain knowledge.
• LoRA improves LLM efficiency for specific tasks.

Method

FasterPy adapts LLMs for Python code optimization by integrating Retrieval-Augmented Generation (RAG) with a performance-improving code knowledge base and Low-Rank Adaptation (LoRA) for fine-tuning.

In practice

• Optimize Python code for execution speed.
• Apply RAG to LLM-based code generation.
• Utilize LoRA for efficient LLM adaptation.

Topics

• FasterPy
• LLM Code Optimization
• Python Performance
• Retrieval-Augmented Generation
• Low-Rank Adaptation
• Software Engineering

Code references

• WuYue22/fasterpy

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.