Targeted Example Generation for Compilation Errors

TL;DR

TEGCER is an automated tool that helps novice programmers fix compilation errors by matching new errors with relevant past examples using a neural network, achieving high accuracy and providing targeted solutions.

Contribution

The paper introduces TEGCER, a neural network-based system that effectively matches new compilation errors with relevant past examples to assist students.

Findings

Achieved 97.7% Pred@3 accuracy in error classification

Trained on over 15,000 error-repair examples

Provides targeted example-based feedback to students

Abstract

We present TEGCER, an automated feedback tool for novice programmers. TEGCER uses supervised classification to match compilation errors in new code submissions with relevant pre-existing errors, submitted by other students before. The dense neural network used to perform this classification task is trained on 15000+ error-repair code examples. The proposed model yields a test set classification Pred@3 accuracy of 97.7% across 212 error category labels. Using this model as its base, TEGCER presents students with the closest relevant examples of solutions for their specific error on demand.