在软件开发PBL中引入基于AI智能体的规格驱动开发的实践实施报告
Practical Implementation Report on Introducing Spec-Driven Development Using AI Agents in Software Development PBL
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中文总结 AI 辅助
本研究在大三本科生SDPBL课程中实践引入基于AI智能体的SDD，定义四阶段工作流程，发现AI提升实施吞吐量但需教师验证代码理解以维持教育效果。
中文摘要 AI 辅助
近年来，GitHub Copilot、Claude Code等自主AI智能体迅速普及。本研究报告在面向大三本科生的软件开发项目式学习（SDPBL）课程中，引入以AI智能体为前提的软件开发方法论——规格驱动开发（Spec-Driven Development, SDD）的实践实施情况。我们定义了包含调研、规划、实施、评审四个阶段的工作流程，还为SDPBL课程搭建了适配环境，使AI智能体能在各阶段生成文档与代码。我们从学生主观AI使用情况、实施吞吐量、代码理解三个维度分析结果，发现不同开发阶段及团队的AI使用模式存在差异；此外，AI智能体的使用提升了实施吞吐量，但也易导致学生在未充分理解代码的情况下推进开发。本研究表明，在将SDD引入SDPBL时，教师定期验证学生的代码理解并提供恰当反馈，对维持教育效果至关重要。
英文摘要
In recent years, autonomous AI agents such as GitHub Copilot and Claude Code have been rapidly gaining popularity. This study reports on the practical implementation of Spec-Driven Development, a software development methodology premised on AI agents, within a Software Development Project-Based Learning (SDPBL) course for third-year undergraduate students. We defined a workflow consisting of four phases, namely investigation, planning, implementation, and review. We also established an environment tailored for the SDPBL course where AI agents generate documentation and code during each phase. We analyzed the results from three perspectives, namely students' subjective AI usage, implementation throughput, and code comprehension. The analysis reveals that AI usage patterns varied across development phases and teams. Moreover, while AI agent utilization increased implementation throughput, it also tended to encourage students to proceed with development without fully understanding the code. This study demonstrates that regular verification of code comprehension by instructors and appropriate feedback are essential for maintaining educational effectiveness when introducing SDD into SDPBL.
发表机构
- Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
- Osaka Institute of Technology（大阪工业大学）
- Wakayama University（和歌山大学）
机构由 AI 辅助整理，请以论文原文为准。
