We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline.
Descriptive code comments are essential for supporting code comprehension and maintenance.
Object-oriented programming languages enable a hierarchical class structure, which provides rich contextual information to guide code comprehension and synthesis.
For extrinsic evaluation, we show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system which can both detect and resolve inconsistent comments based on code changes.
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code.
We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies.
Comments are an integral part of software development; they are natural language descriptions associated with source code elements.