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CodeEvolve uses LLMs and runtime analysis to enhance code performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CodeEvolve, a new framework that uses Large Language Models (LLMs) to automatically enhance code quality and performance. This system integrates runtime profiling data to identify critical optimization targets, reducing the need for manual analysis. CodeEvolve then generates, evaluates, and refines code edits, ensuring functional correctness through various checks including LLM-based review. In tests, it achieved significant speedups on Java codebases and demonstrated reliable optimization for Salesforce Apex. AI

IMPACT Introduces an automated approach to code optimization, potentially improving developer productivity and software performance.

RANK_REASON This is a research paper detailing a novel framework for code enhancement using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CodeEvolve uses LLMs and runtime analysis to enhance code performance

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This is a research paper detailing a novel framework for code enhancement using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shuchita Singh ·

    CodeEvolve: LLM-Driven Evolutionary Optimization with Runtime-Enriched Target Selection for Multi-Language Code Enhancement

    We present CodeEvolve, an evolutionary framework for improving program performance and code quality with Large Language Models (LLMs). CodeEvolve extends OpenEvolve with runtime-guided target selection, Monte Carlo Tree Search (MCTS), automated code refinement, and language-speci…

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