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The bug growth pattern prediction is a complicated, unrelieved task, whichneeds considerable attention. Advance knowledge of the likely number of bugsdiscovered in the software system helps software developers in designatingsufficient resources at a convenient time. The developers may also use suchinformation to take necessary actions to increase the quality of the system andin turn customer satisfaction. In this study, we examine eight different timeseries forecasting models, including Long Short Term Memory Neural Networks(LSTM), auto-regressive integrated moving average (ARIMA), and Random ForestRegressor. Further, we assess the impact of exogenous variables such assoftware release dates by incorporating those into the prediction models. Weanalyze the quality of long-term prediction for each model based on differentperformance metrics. The assessment is conducted on Mozilla, which is a largeopen-source software application. The dataset is originally mined from Bugzillaand 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 bugrepository. We observe that LSTM is effective when considering long-runpredictions whereas Random Forest Regressor enriched by exogenous variablesperforms better for predicting the number of bugs in the short term.

Predicting the Number of Reported Bugs in a Software Repository