  The automatic collection of stack traces in bug tracking systems is an
integral part of many software projects and their maintenance. However, such
reports often contain a lot of duplicates, and the problem of de-duplicating
them into groups arises. In this paper, we propose a new approach to solve the
deduplication task and report on its use on the real-world data from JetBrains,
a leading developer of IDEs and other software. Unlike most of the existing
methods, which assign the incoming stack trace to a particular group in which a
single most similar stack trace is located, we use the information about all
the calculated similarities to the group, as well as the information about the
timestamp of the stack traces. This approach to aggregating all available
information shows significantly better results compared to existing solutions.
The aggregation improved the results over the state-of-the-art solutions by 15
percentage points in the Recall Rate Top-1 metric on the existing NetBeans
dataset and by 8 percentage points on the JetBrains data. Additionally, we
evaluated a simpler k-Nearest Neighbors approach to aggregation and showed that
it cannot reach the same levels of improvement. Finally, we studied what
features from the aggregation contributed the most towards better quality to
understand which of them to develop further. We publish the implementation of
the suggested approach, and will release the newly collected industrial dataset
upon acceptance to facilitate further research in the area.
