We offer a novel solution for Feedback-ORiEnted Per-
fOrmance Software Testing (FOREPOST) for finding performance
problems automatically by learning and using rules
that describe classes of input data that lead to intensive
computations. FOREPOST is an adaptive, feedback-directed
learning testing system that learns rules from AUT execution
traces and uses these learned rules to select test input
data automatically to find more performance problems in
applications when compared to exploratory random performance
testing [9], [10]. FOREPOST uses runtime monitoring
for a short duration of testing together with machine
learning techniques and automated test scripts to reduce
large amounts of performance-related information collected
during AUT runs to a small number of descriptive rules that
provide insights into properties of test input data that lead
to increased computational loads of applications.