  Large language models (LLMs) have shown impressive effectiveness in various
software engineering tasks, including automated program repair (APR). In this
study, we take a deep dive into automated bug fixing utilizing LLMs. In
contrast to many deep learning-based APR methods that assume known bug
locations, rely on line-level localization tools, or address bug prediction and
fixing in one step, our approach uniquely employs LLMs to predict bug location
at the token level and subsequently utilizes them for bug fixing. This
methodological separation of bug localization and fixing using different LLMs
enables effective integration of diverse contextual information and improved
incorporation of inductive biases. We introduce Toggle: Token-Granulated Bug
Localization and Repair, a comprehensive program repair framework that
integrates a bug localization model, an adjustment unit, and a bug-fixing
model. Toggle takes a buggy function as input and generates a complete
corrected function. We investigate various styles of prompting to the bug
fixing model to identify the most effective prompts that better utilize the
inductive bias and significantly outperform others. Toggle achieves the new
state-of-the-art (SOTA) performance on the CodeXGLUE code refinement benchmark,
and exhibits better and comparable performance on several other widely-used APR
datasets, including Defects4J.
