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Abstract

Automatic program correction is an active topic of research, which holds the
potential of dramatically improving productivity of programmers during the
software development process and correctness of software in general. Recent
advances in machine learning, deep learning and NLP have rekindled the hope to
eventually fully automate the process of repairing programs. A key challenge is
ambiguity, as multiple codes -- or fixes -- can implement the same
functionality. In addition, datasets by nature fail to capture the variance
introduced by such ambiguities. Therefore, we propose a deep generative model
to automatically correct programming errors by learning a distribution of
potential fixes. Our model is formulated as a deep conditional variational
autoencoder that samples diverse fixes for the given erroneous programs. In
order to account for ambiguity and inherent lack of representative datasets, we
propose a novel regularizer to encourage the model to generate diverse fixes.
Our evaluations on common programming errors show for the first time the
generation of diverse fixes and strong improvements over the state-of-the-art
approaches by fixing up to 65% of the mistakes.