鞍山师范学院学报

随着人工智能技术与大语言模型的快速迭代，以GitHub Copilot为代表的生成式编程助手，凭借智能代码补全、自然语言交互、测试用例生成、代码调试及重构等核心能力，在提升编码效率、降低学习门槛、优化研发时间分配、激发开发者创造力等方面展现出显著价值。然而，AI生成代码普遍存在业务逻辑偏差、边界条件缺失、安全漏洞隐蔽、可维护性不足等问题。针对上述问题，基于人机协同理论与现代软件工程理论，从人机协同分工机制构建、AI代码审查流程重构、代码质量评估体系建设、全链路工具研发、组织管理机制完善五个维度，提出了系统化、可落地的应用优化策略，从而最大化发挥AI工具效能，实现高效、稳定、可持续的人机协同开发。

With the rapid evolution of artificial intelligence and large language models,as a typical generative programming assistant,GitHub Copilot demonstrates significant value in improving coding efficiency,lowering the threshold for learning new technologies,optimizing R&D time allocation,and releasing developers′ creativity with its core capabilities such as intelligent code completion,natural language interaction,test case generation,code debugging and refactoring.Furthermore,the widespread use of the tools will weaken internal knowledge sharing,experience inheritance and collaboration atmosphere,leading to increased code review costs and slowdown of team technology accumulation.To solve these problems,based on human-machine collaboration theory and modern software engineering theory,this paper proposes systematic and implementable optimization strategies from four dimensions:human-AI collaborative division of labor,AI code review process reconstruction,code quality evaluation system construction,and organizational management mechanism balancing efficiency and collaboration,to maximize the efficiency of AI tools and to achieve efficient,stable,and sustainable human-AI collaborative development.