Problem
Research questions and friction points this paper is trying to address.
Spec-Driven Development
AI Agents
Software Development Project-Based Learning
Code Comprehension
Implementation Throughput
Practical Implementation Report on Introducing Spec-Driven Development Using AI Agents in Software Development PBL
🤖 AI Summary
研究通过在软件开发项目课程中引入基于AI代理的规范驱动开发方法，解决了教育效果维持问题，采用定制化环境和四阶段工作流程，并强调教师定期验证代码理解的重要性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Spec-Driven Development
AI Agents
Software Development PBL
Code Comprehension
Educational Effectiveness
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