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Software Defect Trend Forecasting In Open Source Projects using A Univariate ARIMA Model and FBProphet
Michael Thomas Shrove, Emil Jovanov
Pages - 1 - 15     |    Revised - 31-03-2020     |    Published - 30-04-2020
Volume - 8   Issue - 1    |    Publication Date - April 2020  Table of Contents
Software Engineering, Software Defects, Time Series Forecasting, ARIMA, FBProphet.
Our objective in this research is to provide a framework that will allow project managers, business owners, and developers an effective way to forecast the trend in software defects within a software project in real-time. By providing these stakeholders with a mechanism for forecasting defects, they can then provide the necessary resources at the right time in order to remove these defects before they become too much ultimately leading to software failure. In our research, we will not only show general trends in several open-source projects but also show trends in daily, monthly, and yearly activity. Our research shows that we can use this forecasting method up to 6 months out with only an MSE of 0.019. In this paper, we present our technique and methodologies for developing the inputs for the proposed model and the results of testing on seven open source projects. Further, we discuss the prediction models, the performance, and the implementation using the FBProphet framework and the ARIMA model.
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Mr. Michael Thomas Shrove
Millennium Corporation, Huntsville - United States of America
Dr. Emil Jovanov
ECE Department, University of Alabama, Huntsville - United States of America