Targeted Example Generation for Compilation Errors

09/02/2019

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by   Umair Z. Ahmed, et al.

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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 Using this model as its base, TEGCER presents students with the closest relevant examples of solutions for their specific error on demand.