Omar Javed
TDB, IT, UU
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 more problematic when test inputs are constantly growing which is the case with a large-scale scientific organization such as CERN that generates a plethora of data used by physicists to conduct experiments leading to new scientific discoveries. Therefore, 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 updates of a web service which is used at CERN and we found that the recommendation made by our approach are quite accurate.