  The bug growth pattern prediction is a complicated, unrelieved task, which
needs considerable attention. Advance knowledge of the likely number of bugs
discovered in the software system helps software developers in designating
sufficient resources at a convenient time. The developers may also use such
information to take necessary actions to increase the quality of the system and
in turn customer satisfaction. In this study, we examine eight different time
series forecasting models, including Long Short Term Memory Neural Networks
(LSTM), auto-regressive integrated moving average (ARIMA), and Random Forest
Regressor. Further, we assess the impact of exogenous variables such as
software release dates by incorporating those into the prediction models. We
analyze the quality of long-term prediction for each model based on different
performance metrics. The assessment is conducted on Mozilla, which is a large
open-source software application. The dataset is originally mined from Bugzilla
and contains the number of bugs for the project between Jan 2010 and Dec 2019.
Our numerical analysis provides insights on evaluating the trends in a bug
repository. We observe that LSTM is effective when considering long-run
predictions whereas Random Forest Regressor enriched by exogenous variables
performs better for predicting the number of bugs in the short term.
