  Static Analysis (SA) tools are used to identify potential weaknesses in code
and fix them in advance, while the code is being developed. In legacy codebases
with high complexity, these rules-based static analysis tools generally report
a lot of false warnings along with the actual ones. Though the SA tools uncover
many hidden bugs, they are lost in the volume of fake warnings reported. The
developers expend large hours of time and effort in identifying the true
warnings. Other than impacting the developer productivity, true bugs are also
missed out due to this challenge. To address this problem, we propose a Machine
Learning (ML)-based learning process that uses source codes, historic commit
data, and classifier-ensembles to prioritize the True warnings from the given
list of warnings. This tool is integrated into the development workflow to
filter out the false warnings and prioritize actual bugs. We evaluated our
approach on the networking C codes, from a large data pool of static analysis
warnings reported by the tools. Time-to-time these warnings are addressed by
the developers, labelling them as authentic bugs or fake alerts. The ML model
is trained with full supervision over the code features. Our results confirm
that applying deep learning over the traditional static analysis reports is an
assuring approach for drastically reducing the false positive rates.
