Automated Clustering and Program Repair for Introductory Programming Assignments

TL;DR

This paper introduces a fully automated program repair method that leverages correct student solutions to provide scalable, high-quality feedback for introductory programming assignments, especially useful in large online courses.

Contribution

The paper presents a novel automated repair algorithm that uses existing correct solutions to repair incorrect attempts, enabling scalable feedback in programming education.

Findings

97% of incorrect attempts repaired

81% of repairs are small and high-quality

Promising user study results with an average usefulness grade of 3.4

Abstract

Providing feedback on programming assignments is a tedious task for the instructor, and even impossible in large Massive Open Online Courses with thousands of students. Previous research has suggested that program repair techniques can be used to generate feedback in programming education. In this paper, we present a novel fully automated program repair algorithm for introductory programming assignments. The key idea of the technique, which enables automation and scalability, is to use the existing correct student solutions to repair the incorrect attempts. We evaluate the approach in two experiments: (I) We evaluate the number, size and quality of the generated repairs on 4,293 incorrect student attempts from an existing MOOC. We find that our approach can repair 97% of student attempts, while 81% of those are small repairs of good quality. (II) We conduct a preliminary user study on performance and repair usefulness in an interactive teaching setting. We obtain promising initial results (the average usefulness grade 3.4 on a scale from 1 to 5), and conclude that our approach can be used in an interactive setting.