  Automatically fixing software bugs is a challenging task. While recent work
showed that natural language context is useful in guiding bug-fixing models,
the approach required prompting developers to provide this context, which was
simulated through commit messages written after the bug-fixing code changes
were made. We instead propose using bug report discussions, which are available
before the task is performed and are also naturally occurring, avoiding the
need for any additional information from developers. For this, we augment
standard bug-fixing datasets with bug report discussions. Using these newly
compiled datasets, we demonstrate that various forms of natural language
context derived from such discussions can aid bug-fixing, even leading to
improved performance over using commit messages corresponding to the oracle
bug-fixing commits.
