Automated generation of feedback on programming assignments holds significant
benefits for programming education, especially when it comes to advanced
assignments. Automated Program Repair techniques, especially Large Language
Model based approaches, have gained notable recognition for their potential to
fix introductory assignments. However, the programs used for evaluation are
relatively simple. It remains unclear how existing approaches perform in
repairing programs from higher-level programming courses. To address these
limitations, we curate a new advanced student assignment dataset named
Defects4DS from a higher-level programming course. Subsequently, we identify
the challenges related to fixing bugs in advanced assignments. Based on the
analysis, we develop a framework called PaR that is powered by the LLM. PaR
works in three phases: Peer Solution Selection, Multi-Source Prompt Generation,
and Program Repair. Peer Solution Selection identifies the closely related peer
programs based on lexical, semantic, and syntactic criteria. Then Multi-Source
Prompt Generation adeptly combines multiple sources of information to create a
comprehensive and informative prompt for the last Program Repair stage. The
evaluation on Defects4DS and another well-investigated ITSP dataset reveals
that PaR achieves a new state-of-the-art performance, demonstrating impressive
improvements of 19.94% and 15.2% in repair rate compared to prior
state-of-the-art LLM- and symbolic-based approaches, respectively

我们基于大型语言模型（LLM）提出了一个名为 PaR 的框架，用于解决高级编程作业中的程序错误修复问题，并通过对新的高级学生作业数据集（Defects4DS）和另一个经过深入调查的 ITSP 数据集的评估，展示了超越现有 LMM - 和基于符号的方法的最新性能提升（分别达到了 19.94％和 15.2％的修复率提高）。