  After a developer submits code, corresponding test cases arise to ensure the
quality of software delivery. Test failures would occur during this period,
such as crash, error, and timeout. Since it takes time for developers to
resolve them, many duplicate failures will happen during this period. In the
delivery practice of SAP HANA, crash triage is considered as the most
time-consuming task. If duplicate crash failures can be automatically
identified, the degree of automation will be significantly enhanced. To find
such duplicates, we propose a training-based mathematical model that utilizes
component information of SAP HANA to achieve better crash similarity
comparison. We implement our approach in a tool named Knowledge-based Detector
(K-Detector), which is verified by 11,208 samples and performs 0.986 in AUC.
Furthermore, we have deployed K-Detector to the production environment, and it
can save 97% human efforts in crash triage as statistics.
