Data-Driven Feedback Generation for Introductory Programming Exercises

TL;DR

This paper presents SARFGEN, a data-driven framework that automates feedback generation for introductory programming exercises at scale, achieving high accuracy and efficiency by leveraging large student submission datasets.

Contribution

The paper introduces SARFGEN, a novel, fully automated, problem-agnostic program repair system that effectively provides feedback for large-scale programming courses.

Findings

Generates feedback for 89.7% of incorrect submissions within two seconds

Successfully deployed in a real MOOC setting with thousands of student attempts

Outperforms existing techniques in speed and coverage

Abstract

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.