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We present a method for automatically generating repair feedback for syntaxerrors for introductory programming problems. Syntax errors constitute one ofthe largest classes of errors (34%) in our dataset of student submissionsobtained from a MOOC course on edX. The previous techniques for generatingautomated feed- back on programming assignments have focused on functionalcorrectness and style considerations of student programs. These techniquesanalyze the program AST of the program and then perform some dynamic andsymbolic analyses to compute repair feedback. Unfortunately, it is not possibleto generate ASTs for student pro- grams with syntax errors and therefore theprevious feedback techniques are not applicable in repairing syntax errors.  We present a technique for providing feedback on syntax errors that usesRecurrent neural networks (RNNs) to model syntactically valid token sequences.Our approach is inspired from the recent work on learning language models fromBig Code (large code corpus). For a given programming assignment, we firstlearn an RNN to model all valid token sequences using the set of syntacticallycorrect student submissions. Then, for a student submission with syntax errors,we query the learnt RNN model with the prefix to- ken sequence to predict tokensequences that can fix the error by either replacing or inserting the predictedtoken sequence at the error location. We evaluate our technique on over 14, 000student submissions with syntax errors. Our technique can completely re- pair31.69% (4501/14203) of submissions with syntax errors and in addition partiallycorrect 6.39% (908/14203) of the submissions.

Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks