  Software performance testing requires a set of inputs that exercise different
sections of the code to identify performance issues. However, running tests on
a large set of inputs can be a very time-consuming process. It is even more
problematic when test inputs are constantly growing, which is the case with a
large-scale scientific organization such as CERN where the process of
performing scientific experiment generates plethora of data that is analyzed by
physicists leading to new scientific discoveries. Therefore, in this article,
we present a test input minimization approach based on a clustering technique
to handle the issue of testing on growing data. Furthermore, we use clustering
information to propose an approach that recommends the tester to decide when to
run the complete test suite for performance testing. To demonstrate the
efficacy of our approach, we applied it to two different code updates of a web
service which is used at CERN and we found that the recommendation for
performance test initiation made by our approach for an update with bottleneck
is valid.
