  Capturing customer workloads of database systems to replay these workloads
during internal testing can be beneficial for software quality assurance.
However, we experienced that such replays can produce a large amount of false
positive alerts that make the results unreliable or time consuming to analyze.
Therefore, we design a machine learning based approach that attributes root
causes to the alerts. This provides several benefits for quality assurance and
allows for example to classify whether an alert is true positive or false
positive. Our approach considerably reduces manual effort and improves the
overall quality assurance for the database system SAP HANA. We discuss the
problem, the design and result of our approach, and we present practical
limitations that may require further research.
