Towards Automated Anomaly Report Assignment in Large Complex Systems Using Stacked Generalization

Abstract

Maintenance costs can be substantial for organizations with very large and complex software systems. This paper describes research for reducing anomaly report turnaround time which, if successful, would contribute to reducing maintenance costs and at the same time maintaining a good customer perception. Specifically, we are addressing the problem of the manual, laborious, and inaccurate process of assigning anomaly reports to the correct design teams. In large organizations with complex systems this is particularly problematic because the receiver of the anomaly report from customer may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be wrongly routed around in the organization causing delays and unnecessary work. We have developed and validated machine learning approach, based on stacked generalization, to automatically route anomaly reports to the correct design teams in the organization. A research prototype has been implemented and evaluated on roughly one year of real anomaly reports on a large and complex system at Ericsson AB. The prediction accuracy of the automation is approaching that of humans, indicating that the anomaly report handling time could be significantly reduced by using our approach.