Speaker
Description
Software testing makes up a significant part of software development processes. This is especially true in the case of a complex IT system like IP Multimedia Subsystem (IMS). Our case study describes machine learning and visual analytics approaches to support a non-functional performance test, the endurance test. This test checks whether the software can continuously work without performance degradation. The analysis of data generated by such tests is a challenge itself due to their high complexity and contextual nature. An additional problem can be that often only a fraction of the data is annotated. Our approach includes different one-class classification models providing inputs for visualizations. We have also studied the problem of the interpretability of results in case of failed tests and tried to trace what may have gone wrong on the basis of information gained from the models.
