  As machine learning tools progress, the inevitable question arises: How can
machine learning help us write better code? With significant progress being
achieved in natural language processing with models like GPT-3 and Bert, the
applications of natural language processing techniques to code are starting to
be explored. Most of the research has been focused on automatic program repair
(APR), and while the results on synthetic or highly filtered datasets are
promising, such models are hard to apply in real-world scenarios because of
inadequate bug localization. We propose BigIssue: a benchmark for realistic bug
localization. The goal of the benchmark is two-fold. We provide (1) a general
benchmark with a diversity of real and synthetic Java bugs and (2) a motivation
to improve bug localization capabilities of models through attention to the
full repository context. With the introduction of BigIssue, we hope to advance
the state of the art in bug localization, in turn improving APR performance and
increasing its applicability to the modern development cycle.
