基准测试 ChatGPT、Codeium 和 GitHub Copilot：AI 驱动编程与调试助手的比较研究
Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants
AI总结：
本文比较了 ChatGPT、Codeium 和 GitHub Copilot 在 LeetCode 问题上的表现，发现 Copilot 擅长简单和中等任务，ChatGPT 在内存效率和调试上更优，而 Codeium 在复杂问题上表现较弱，为开发者选择 AI 编程助手提供了参考。
摘要：
随着 AI 驱动工具在软件开发中的应用日益广泛，大语言模型（LLMs）已成为代码生成、缺陷修复和优化等任务的重要工具。ChatGPT、GitHub Copilot 和 Codeium 等工具在解决编程挑战方面提供了有价值的帮助，但它们的有效性仍未得到充分探索。本文对 ChatGPT、Codeium 和 GitHub Copilot 进行了比较研究，评估它们在 LeetCode 问题上不同难度级别和类别下的表现。研究评估了成功率、运行时间效率、内存使用和错误处理能力等关键指标。GitHub Copilot 在简单和中等难度的任务上表现更优，而 ChatGPT 在内存效率和调试方面表现出色。Codeium 虽然具有潜力，但在更复杂的问题上表现吃力。尽管各有优势，所有工具在处理较难问题时都面临挑战。这些见解有助于更深入地理解每个工具的能力和局限性，为寻求在编码工作流中优化 AI 集成的开发者和研究人员提供指导。
英文摘要：
With the increasing adoption of AI-driven tools in software development, large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. Tools like ChatGPT, GitHub Copilot, and Codeium provide valuable assistance in solving programming challenges, yet their effectiveness remains underexplored. This paper presents a comparative study of ChatGPT, Codeium, and GitHub Copilot, evaluating their performance on LeetCode problems across varying difficulty levels and categories. Key metrics such as success rates, runtime efficiency, memory usage, and error-handling capabilities are assessed. GitHub Copilot showed superior performance on easier and medium tasks, while ChatGPT excelled in memory efficiency and debugging. Codeium, though promising, struggled with more complex problems. Despite their strengths, all tools faced challenges in handling harder problems. These insights provide a deeper understanding of each tool's capabilities and limitations, offering guidance for developers and researchers seeking to optimize AI integration in coding workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
