Generative AI technologies promise to transform the product development lifecycle. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot, an AI-powered coding assistant. We identified 15 software development tasks and assessed Copilot's benefits through real-world projects on large proprietary code bases. Our findings indicate significant reductions in developer toil, with up to 50% time saved in code documentation and autocompletion, and 30-40% in repetitive coding tasks, unit test generation, debugging, and pair programming. However, Copilot struggles with complex tasks, large functions, multiple files, and proprietary contexts, particularly with C/C++ code. We project a 33-36% time reduction for coding-related tasks in a cloud-first software development lifecycle. This study aims to quantify productivity improvements, identify underperforming scenarios, examine practical benefits and challenges, investigate performance variations across programming languages, and discuss emerging issues related to code quality, security, and developer experience.

翻译：生成式人工智能技术有望变革产品开发生命周期。本研究评估了使用GitHub Copilot（一款AI驱动的编码助手）所获得的效率提升、改进空间及新兴挑战。我们识别了15项软件开发任务，并通过大型专有代码库的实际项目评估了Copilot的效益。研究结果表明，该工具显著减轻了开发人员的重复性劳动：在代码文档撰写和自动补全方面节省时间高达50%，在重复性编码任务、单元测试生成、调试及结对编程方面节省30-40%的时间。然而，Copilot在处理复杂任务、大型函数、多文件场景及专有代码环境时仍存在困难，尤其在C/C++代码中表现更为明显。我们预测在云优先的软件开发生命周期中，编码相关任务的时间可减少33-36%。本研究旨在量化生产力提升效果，识别表现欠佳的场景，检验实际效益与挑战，探究跨编程语言的性能差异，并讨论与代码质量、安全性和开发者体验相关的新兴问题。