  Tracking user reported bugs requires considerable engineering effort in going
through many repetitive reports and assigning them to the correct teams. This
paper proposes a neural architecture that can jointly (1) detect if two bug
reports are duplicates, and (2) aggregate them into latent topics. Leveraging
the assumption that learning the topic of a bug is a sub-task for detecting
duplicates, we design a loss function that can jointly perform both tasks but
needs supervision for only duplicate classification, achieving topic clustering
in an unsupervised fashion. We use a two-step attention module that uses
self-attention for topic clustering and conditional attention for duplicate
detection. We study the characteristics of two types of real world datasets
that have been marked for duplicate bugs by engineers and by non-technical
annotators. The results demonstrate that our model not only can outperform
state-of-the-art methods for duplicate classification on both cases, but can
also learn meaningful latent clusters without additional supervision.
