We propose a method combining machine learning with a static analysis tool
(i.e. Infer) to automatically repair source code. Machine Learning methods
perform well for producing idiomatic source code. However, their output is
sometimes difficult to trust as language models can output incorrect code with
high confidence. Static analysis tools are trustable, but also less flexible
and produce non-idiomatic code. In this paper, we propose to fix resource leak
bugs in IR space, and to use a sequence-to-sequence model to propose fix in
source code space. We also study several decoding strategies, and use Infer to
filter the output of the model. On a dataset of CodeNet submissions with
potential resource leak bugs, our method is able to find a function with the
same semantics that does not raise a warning with around 97% precision and 66%
recall.Comment: 13 pages. DL4C 202