Navigating Information Overload Caused by Automated Testing - a Clustering Approach in Multi-Branch Development

Background. Test automation is a widely used technique to increase the efficiency of software testing. However, executing more test cases increases the effort required to analyze test results. At Qlik, automated tests run nightly for up to 20 development branches, each containing thousands of test cases, resulting in information overload. Aim. We therefore develop a tool that supports the analysis of test results. Method. We create NIOCAT, a tool that clusters similar test case failures, to help the analyst identify underlying causes. To evaluate the tool, experiments on manually created subsets of failed test cases representing different use cases are conducted, and a focus group meeting is held with test analysts at Qlik. Results. The case study shows that NIOCAT creates accurate clusters, in line with analyses performed by human analysts. Further, the potential time-savings of our approach is confirmed by the participants in the focus group. Conclusions. NIOCAT provides a feasible complement to current automated testing practices at Qlik by reducing information overload.