  Software bugs cost the global economy billions of dollars each year and take
up ~50% of the development time. Once a bug is reported, the assigned developer
attempts to identify and understand the source code responsible for the bug and
then corrects the code. Over the last five decades, there has been significant
research on automatically finding or correcting software bugs. However, there
has been little research on automatically explaining the bugs to the
developers, which is essential but a highly challenging task. In this paper, we
propose Bugsplainer, a novel web-based debugging solution that generates
natural language explanations for software bugs by learning from a large corpus
of bug-fix commits. Bugsplainer leverages code structures to reason about a bug
and employs the fine-tuned version of a text generation model, CodeT5, to
generate the explanations.
  Tool video: https://youtu.be/xga-ScvULpk
