  Flaw-finding static analysis tools typically generate large volumes of code
flaw alerts including many false positives. To save on human effort to triage
these alerts, a significant body of work attempts to use machine learning to
classify and prioritize alerts. Identifying a useful set of training data,
however, remains a fundamental challenge in developing such classifiers in many
contexts. We propose using static analysis test suites (i.e., repositories of
"benchmark" programs that are purpose-built to test coverage and precision of
static analysis tools) as a novel source of training data. In a case study, we
generated a large quantity of alerts by executing various static analyzers on
the Juliet C/C++ test suite, and we automatically derived ground truth labels
for these alerts by referencing the Juliet test suite metadata. Finally, we
used this data to train classifiers to predict whether an alert is a false
positive. Our classifiers obtained high precision (90.2%) and recall (88.2%)
for a large number of code flaw types on a hold-out test set. This preliminary
result suggests that pre-training classifiers on test suite data could help to
jumpstart static analysis alert classification in data-limited contexts.
