一句话：提出上下文感知的数据驱动修复算法 Cafe，为编程作业生成反馈，在 OCaml 学生程序中修复 83% 错误提交。
针对编程练习的个人化反馈，现有数据驱动方法通常依赖一个包含正确程序的语料库，通过相似参考程序修复错误提交，但该做法假设语料库中存在与错误提交足够接近的解答，限制了适用范围。本文提出 Cafe，一种新的数据驱动反馈生成方法，其核心是上下文感知的修复算法，即使错误程序与参考解答差异显著，也能利用修复上下文生成反馈。作者为 OCaml 实现 Cafe，并在 4,211 个真实学生程序上评估。结果显示，Cafe 能修复 83% 的错误提交，显著优于现有方法。
Recently, various techniques have been proposed to automatically provide personalized feedback on programming exercises. The cutting edge of which is the data-driven approaches that leverage a corpus of existing correct programs and repair incorrect submissions by using similar reference programs in the corpus. However, current data-driven techniques work under the strong assumption that the corpus contains a solution program that is close enough to the incorrect submission. In this paper, we present Cafe, a new data-driven approach for feedback generation that overcomes this limitation. Unlike existing approaches, Cafe uses a novel context-aware repair algorithm that can generate feedback even if the incorrect program differs significantly from the reference solutions. We implemented Cafe for OCaml and evaluated it with 4,211 real student programs. The results show that Cafe is able to repair 83 % of incorrect submissions, far outperforming existing approaches.
DOI 原文 ·
@inproceedings{paperbot6320,
title = {Context-aware and data-driven feedback generation for programming assignments},
author = {Dowon Song and Woosuk Lee and Hakjoo Oh},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468598}
}