# Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2501.16692 · HTML: https://mlsanity.com/paper/2501.16692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG for code, one of 15 directions in Retrieval-augmented generation.
- Builds on 30 of its 37 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Placed in

- Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/)

## Builds on (most cited first, 30 of 30)

- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [In-Context Unlearning: Language Models as Few Shot Unlearners](https://mlsanity.com/paper/2310.07579/) (2023)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [What Makes Good Examples for Visual In-Context Learning?](https://mlsanity.com/paper/2301.13670/) (2023)
- [Compositional Exemplars for In-context Learning](https://mlsanity.com/paper/2302.05698/) (2023)
- [CodePlan: Repository-level Coding using LLMs and Planning](https://mlsanity.com/paper/2309.12499/) (2023)
- [The Learnability of In-Context Learning](https://mlsanity.com/paper/2303.07895/) (2023)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [Finding Support Examples for In-Context Learning](https://mlsanity.com/paper/2302.13539/) (2023)
- [CodeRAG-Bench: Can Retrieval Augment Code Generation?](https://mlsanity.com/paper/2406.14497/) (2024)
- [LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding](https://mlsanity.com/paper/2306.14924/) (2023)
- [Enchanting Program Specification Synthesis by Large Language Models using Static Analysis and Program Verification](https://mlsanity.com/paper/2404.00762/) (2024)
- [How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark](https://mlsanity.com/paper/2406.06647/) (2024)
- [Search-Based LLMs for Code Optimization](https://mlsanity.com/paper/2408.12159/) (2024)
- [Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization](https://mlsanity.com/paper/2402.14182/) (2024)
- [Prompt-based Code Completion via Multi-Retrieval Augmented Generation](https://mlsanity.com/paper/2405.07530/) (2024)
- [Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.19619/) (2024)
- [Utilizing Deep Learning to Optimize Software Development Processes](https://mlsanity.com/paper/2404.13630/) (2024)
- [Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents](https://mlsanity.com/paper/2310.08837/) (2023)
- [JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization](https://mlsanity.com/paper/2312.02213/) (2023)
- [EyeTrans: Merging Human and Machine Attention for Neural Code Summarization](https://mlsanity.com/paper/2402.14096/) (2024)
- [Modeling Programmer Attention as Scanpath Prediction](https://mlsanity.com/paper/2308.13920/) (2023)
- [Pre-Training Representations of Binary Code Using Contrastive Learning](https://mlsanity.com/paper/2210.05102/) (2022)
- [RAG-Enhanced Commit Message Generation](https://mlsanity.com/paper/2406.05514/) (2024)
- [Frustrated with Code Quality Issues? LLMs can Help!](https://mlsanity.com/paper/2309.12938/) (2023)
- [Leveraging Artificial Intelligence on Binary Code Comprehension](https://mlsanity.com/paper/2210.05103/) (2022)
- [MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning](https://mlsanity.com/paper/2409.13213/) (2024)
- [Demo-Craft: Using In-Context Learning to Improve Code Generation in Large Language Models](https://mlsanity.com/paper/2411.00865/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning Game-Playing Agents with Generative Code Optimization](https://mlsanity.com/paper/2508.19506/) (2025)

## Abstract (arXiv)

Optimizing software performance through automated code refinement offers a promising avenue for enhancing execution speed and efficiency. Despite recent advancements in LLMs, a significant gap remains in their ability to perform in-depth program analysis. This study introduces AUTOPATCH, an in-context learning approach designed to bridge this gap by enabling LLMs to automatically generate optimized code. Inspired by how programmers learn and apply knowledge to optimize software, AUTOPATCH incorporates three key components: (1) an analogy-driven framework to align LLM optimization with human cognitive processes, (2) a unified approach that integrates historical code examples and CFG analysis for context-aware learning, and (3) an automated pipeline for generating optimized code through in-context prompting. Experimental results demonstrate that AUTOPATCH achieves a 7.3% improvement in execution efficiency over GPT-4o across common generated executable code, highlighting its potential to advance automated program runtime optimization.