This paper introduces the "Search, Align, and Repair" data-driven program
repair framework to automate feedback generation for introductory programming
exercises. Distinct from existing techniques, our goal is to develop an
efficient, fully automated, and problem-agnostic technique for large or
MOOC-scale introductory programming courses. We leverage the large amount of
available student submissions in such settings and develop new algorithms for
identifying similar programs, aligning correct and incorrect programs, and
repairing incorrect programs by finding minimal fixes. We have implemented our
technique in the SARFGEN system and evaluated it on thousands of real student
attempts from the Microsoft-DEV204.1X edX course and the Microsoft CodeHunt
platform. Our results show that SARFGEN can, within two seconds on average,
generate concise, useful feedback for 89.7% of the incorrect student
submissions. It has been integrated with the Microsoft-DEV204.1X edX class and
deployed for production use.

本文介绍了 “搜索，对齐和修复” 数据驱动程序修复框架，旨在自动化初级编程练习的反馈生成，采用了大量的学生提交。该技术可在大型或 MOOC 规模的初级编程课程中使用，其结果表明，在平均 2 秒内，SARFGEN 系统可以为 89.7％不正确的学生提交生成简明有用的反馈，并已集成到 Microsoft-DEV204.1X edX 班级中，用于生产。