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6 CONCLUSIONS
Software defects must be tackled quickly and proac-
tively because the quality and efficiency of any soft-
ware product are essential features for it to survive on
the market.
One of the main advantages of using the proposed
solution is that it can work with any dataset from any
industry as long as the bug related data is organized
in a particular format - similar to the one mentioned
in our datasets. In addition, this approach gathers de-
tails associated with a specific application component
in a single place. It was developed to find similari-
ties between two or more software bugs that were re-
ported on a particular system so that the data that is
being displayed refers to the same feature or category
of features. Also, most of the time in the development
process is spent on discovering why a bug is present in
the software, how the code base needs to be changed
to fix it, and what other similar issues have been re-
ported in the past. All these answers can be easily
formulated by analyzing the data shown in the web
application module of the presented solution.
The solution presented can be improved and fur-
ther enriched with multiple features so users can ben-
efit even more when using it in their day-to-day ac-
tivities. For example, in this research, we used a pre-
trained model for the BERT algorithm. However, us-
ing an industry-specific vocabulary could benefit even
more from the accuracy of BERT because the more
he learns what different terms mean, the better the
prediction will be. Furthermore, the product require-
ments represent another source of relevant informa-
tion on a specific topic. For the case analyzed in this
paper, the requirements reside inside epic contracts
and epic analysis documents. Integrating this infor-
mation into the dataset used for prediction will en-
large the possibility of finding the root cause of a bug.
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