  We present a novel technique for automatic program correction in MOOCs,
capable of fixing both syntactic and semantic errors without manual, problem
specific correction strategies. Given an incorrect student program, it
generates candidate programs from a distribution of likely corrections, and
checks each candidate for correctness against a test suite.
  The key observation is that in MOOCs many programs share similar code
fragments, and the seq2seq neural network model, used in the natural-language
processing task of machine translation, can be modified and trained to recover
these fragments.
  Experiment shows our scheme can correct 29% of all incorrect submissions and
out-performs state of the art approach which requires manual, problem specific
correction strategies.
