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.
@article{arxiv.1909.00769,
title = {Targeted Example Generation for Compilation Errors},
author = {Umair Z. Ahmed and Renuka Sindhgatta and Nisheeth Srivastava and Amey Karkare},
journal= {arXiv preprint arXiv:1909.00769},
year = {2019}
}
Comments
To appear in 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019)