  Software bugs in a production environment have an undesirable impact on
quality of service, unplanned system downtime, and disruption in good customer
experience, resulting in loss of revenue and reputation. Existing approaches to
automated software bug repair focuses on known bug templates detected using
static code analysis tools and test suites, and in automatic generation of
patch code for these bugs. We describe the typical bug fixing process employed
in the Linux kernel, and motivate the need for a new automated tool flow to fix
bugs. We present an initial design of such an automated tool that uses
Recurrent Neural Network (RNN) based Natural Language Processing to generate
patch recommendations from user generated bug reports. At the 50th percentile
of the test bugs, the correct patch occurs within the top 11.5 patch
recommendations output by the model. Further, we present a Linux kernel
developer's assessment of the quality of patches recommended for new unresolved
kernel bugs.
