Static analysis tools report software defects that may or may not be detected by
other verification methods. Two challenges complicating the adoption of these tools
are spurious false positive warnings and legitimate warnings that are not acted on.
This paper reports automated support to help address these challenges using
logistic regression models that predict the foregoing types of warnings from
signals in the warnings and implicated code. Because examining many potential
signaling factors in large software development settings can be expensive, we use a
screening methodology to quickly discard factors with low predictive power and
cost-effectively build predictive models. Our empirical evaluation indicates that
these models can achieve high accuracy in predicting accurate and actionable static
analysis warnings, and suggests that the models are competitive with alternative
models built without screening.