For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.