  The exercise of detecting similar bug reports in bug tracking systems is
known as duplicate bug report detection. Having prior knowledge of a bug
report's existence reduces efforts put into debugging problems and identifying
the root cause. Rule and Query-based solutions recommend a long list of
potential similar bug reports with no clear ranking. In addition, triage
engineers are less motivated to spend time going through an extensive list.
Consequently, this deters the use of duplicate bug report retrieval solutions.
In this paper, we have proposed a solution using a combination of NLP
techniques. Our approach considers unstructured and structured attributes of a
bug report like summary, description and severity, impacted products,
platforms, categories, etc. It uses a custom data transformer, a deep neural
network, and a non-generalizing machine learning method to retrieve existing
identical bug reports. We have performed numerous experiments with significant
data sources containing thousands of bug reports and showcased that the
proposed solution achieves a high retrieval accuracy of 70% for recall@5.
