Clustering of back-end failures in automated testing

Sammanfattning: Automated Software testing is becoming increasingly popular, which in turn creates more information that has to be analyzed. At the software company Qlik a tool called NIOCAT is used to create clusters of failed test cases thought to originate from the same code defect. The clustering is done in order to decrease the ever increasing amount of manual analysis needed to be done with regards to software testing. However, the existing tool currently only clusters by using information from the front-end of the system under test. This makes clusterings harder to create when the code defects which cause the tests to fail are originating from the back-end. In this thesis we have looked into different types of back-end information and different methods for using this information in order to create clusters of failed test case executions originating from the same code defect. We created a prototype that clusters failed test case executions by analyzing methods names used in requests sent to the server. We did this using the vector space model in which we evaluated multiple approaches for weighting terms. The best approach seemed to be weighting the methods using a suspiciousness rating. The prototype shows great promise of working well at Qlik but further work and research has to be done to be conclusive.