Forecasting field defect rates using a combined time-based and metrics-based approach: a case study of OpenBSD

Open source software systems are critical infrastructure for many applications; however, little has been precisely measured about their quality. Forecasting the field defect-occurrence rate over the entire lifespan of a release before deployment for open source software systems may enable informed decision-making. In this paper, we present an empirical case study often releases of OpenBSD. We use the novel approach of predicting model parameters of software reliability growth models (SRGMs) using metrics-based modeling methods. We consider three SRGMs, seven metrics-based prediction methods, and two different sets of predictors. Our results show that accurate field defect-occurrence rate forecasts are possible for OpenBSD, as measured by the Theil forecasting statistic. We identify the SRGM that produces the most accurate forecasts and subjectively determine the preferred metrics-based prediction method and set of predictors. Our findings are steps towards managing the risks associated with field defects