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LLM-Generated Code vs Human Code Benchmarking

Overview

This project evaluates the performance and correctness of LLM-generated code compared to human-written implementations.

Objectives

  • Measure runtime performance
  • Analyze memory usage
  • Validate correctness on algorithmic problems

Methodology

Implemented automated benchmarking and testing pipelines using Python.

Technologies

  • Python
  • Large Language Models
  • Algorithms
  • Performance Analysis

Project Type

Academic / Research Project

About

Benchmarking LLM-generated code against human-written implementations using performance and correctness metrics.

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