Abstract: To address the limitations of traditional programming feedback,which relies heavily on outcome-based metrics and lacks fine-grained guidance,and to tackle the challenges of generalized application and insufficient guidance of large language mo-dels (LLMs) in educational contexts,this paper constructs an automated system for generating student learning reports based on code data.The system innovatively integrates static code quality analysis with multiple code submission records,and specifically employs a multi-role Agent collaborative model,optimized Chain-of-Thought (CoT) prompting strategies,and a hierarchical gene-ration mechanism to provide students with precise and comprehensive learning feedback.Through an empirical study using real student programming data,the results demonstrate that the system can effectively pinpoint specific issues in students' code,clearly revealing their problem-solving thought processes and knowledge gaps.Student evaluation feedback confirms that the generated learning reports exhibit excellent performance in terms of accuracy and practicality,showcasing significant application value and development potential in programming teaching practice.

Key words: Programming feedback, Static code analysis, Fine-grained report generation, Chain-of-Thought, Agent