Automatic testing constitutes an important part of everyday development practice. Worldline, a major IT company, is creating more and more tests to ensure the good behaviour of its applications and gain in efficiency and quality. But running all these tests may take hours. For this reason tests are not launched as often as they should and are mostly run at night. The company wishes to improve its development and testing process by giving to developers rapid feedback after a change. An interesting solution is to reduce the number of tests to run by identifying only those exercising the piece of code changed. Two main approaches are proposed in the literature: static and dynamic. The static approach creates a model of the source code and explores it to find links between changed methods and tests. The dynamic approach records invocations of methods during the execution of test scenarios. Moose, a tool allowing to create static models of source code is a good candidate to carry this approach. Thanks to the partnership created with Worldline, we investigate on three industrial, closed source, cases to compare static and dynamic approaches. We evaluate the impact on the results of the frequency of modification of methods or considering groups of methods instead of single ones. We found that considering commits instead of individual methods tends to worsen the results, perhaps due to their large size.