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We formulate the novel task of automatically updating an existing naturallanguage comment based on changes in the body of code it accompanies. Wepropose an approach that learns to correlate changes across two distinctlanguage representations, to generate a sequence of edits that are applied tothe existing comment to reflect the source code modifications. We train andevaluate our model using a dataset that we collected from commit histories ofopen-source software projects, with each example consisting of a concurrentupdate to a method and its corresponding comment. We compare our approachagainst multiple baselines using both automatic metrics and human evaluation.Results reflect the challenge of this task and that our model outperformsbaselines with respect to making edits.

Learning to Update Natural Language Comments Based on Code Changes