Predicting Software Field Reliability

Abstract

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.