The objective of the work described is to accurately predict, as early as possible in the software lifecycle, how reliably a new software release will behave in the field. The initiative is based on a set of innovative mathematical models that have consistently shown a high correlation between key in-process metrics and our primary customer experience metric, SWDPMH (Software Defects per Million Hours [usage] per Month). We have focused on the three primary dimensions of testing – incoming, fixed, and backlog bugs. All of the key predictive metrics described here are empirically-derived, and in specific quantitative terms have not previously been documented in the software engineering/quality literature. A key part of this work is the empirical determination of the precision of the measurements of the primary predictive variables, and the determination of the prediction (outcome) error. These error values enable teams to accurately gauge bug finding and fixing progress, week by week, during the primary test period.