Finding Predictors of Field Defects for Open Source Software Systems in Commonly Available Data Sources: a Case Study of OpenBSD

Open source software systems are important components of many business software applications. Field defect predictions for open source software systems may allow organizations to make informed decisions regarding open source software components. In this paper, we remotely measure and analyze predictors (metrics available before release) mined from established data sources (the code repository and the request tracking system) as well as a novel source of data (mailing list archives) for nine releases of OpenBSD. First, we attempt to predict field defects by extending a software reliability model fitted to development defects. We find this approach to be infeasible, which motivates examining metrics-based field defect prediction. Then, we evaluate 139 predictors using established statistical methods: Kendall’s rank correlation, Pearson’s rank correlation, and forward AIC model selection. The metrics we collect include product metrics, development metrics, deployment and usage metrics, and software and hardware configurations metrics. We find the number of messages to the technical discussion mailing list during the development period (a deployment and usage metric captured from mailing list archives) to be the best predictor of field defects. Our work identifies predictors of field defects in commonly available data sources for open source software systems and is a step towards metrics-based field defect prediction for quantitatively-based decision making regarding open source software components.