An Empirical Comparison of Field Defect Modeling Methods

Abstract

planning, software insurance In this study, we report empirical results from forecasting field defect rates and predicting the number of field defects for a large commercial software system. We find that we are not able to accurately forecast field defect rates using a combined time-based and metrics-based approach, as judged by the Theil forecasting statistic. We suggest possible conditions that may have contributed to the poor results. Next, we use metrics-based approaches to predict the number of field defects within the six months after deployment. We find that the simple ratios method produce more accurate predictions than more complex metrics-based methods. Our results are steps toward quantitatively managing the risks associated with software field defects.