  IR-based fault localization approaches achieves promising results when
locating faulty files by comparing a bug report with source code.
Unfortunately, they become less effective to locate faulty methods. We conduct
a preliminary study to explore its challenges, and identify three problems: the
semantic gap problem, the representation sparseness problem, and the single
revision problem. To tackle these problems, we propose MRAM, a mixed RNN and
attention model, which combines bug-fixing features and method structured
features to explore both implicit and explicit relevance between methods and
bug reports for method level fault localization task. The core ideas of our
model are: (1) constructing code revision graphs from code, commits and past
bug reports, which reveal the latent relations among methods to augment short
methods and as well provide all revisions of code and past fixes to train more
accurate models; (2) embedding three method structured features (token
sequences, API invocation sequences, and comments) jointly with RNN and soft
attention to represent source methods and obtain their implicit relevance with
bug reports; and (3) integrating multirevision bug-fixing features, which
provide the explicit relevance between bug reports and methods, to improve the
performance. We have implemented MRAM and conducted a controlled experiment on
five open-source projects. Comparing with stateof-the-art approaches, our MRAM
improves MRR values by 3.8- 5.1% (3.7-5.4%) when the dataset contains (does not
contain) localized bug reports. Our statistics test shows that our improvements
are significant
