  Generating tests that can reveal performance issues in large and complex
software systems within a reasonable amount of time is a challenging task. On
one hand, there are numerous combinations of input data values to explore. On
the other hand, we have a limited test budget to execute tests. What makes this
task even more difficult is the lack of access to source code and the internal
details of these systems. In this paper, we present an automated test
generation method called ACTA for black-box performance testing. ACTA is based
on active learning, which means that it does not require a large set of
historical test data to learn about the performance characteristics of the
system under test. Instead, it dynamically chooses the tests to execute using
uncertainty sampling. ACTA relies on a conditional variant of generative
adversarial networks,and facilitates specifying performance requirements in
terms of conditions and generating tests that address those conditions.We have
evaluated ACTA on a benchmark web application, and the experimental results
indicate that this method is comparable with random testing, and two other
machine learning methods,i.e. PerfXRL and DN.
