Many software producers utilize beta programs to predict post-release quality and to ensure that their products meet quality expectations of users. Prior work indicates that software producers need to adjust predictions to account for usage environments and usage scenarios differences between beta populations and post-release populations. However, little is known about how usage characteristics relate to field quality and how usage characteristics differ between beta and post-release. In this study, we examine application crash, application hang, system crash, and usage information from millions of Windows\u00ae users to 1) examine the effects of usage characteristics differences on field quality (e.g. which usage characteristics impact quality), 2) examine usage characteristics differences between beta and post-release (e.g. do impactful usage characteristics differ), and 3) report experiences adjusting field quality predictions for Windows. Among the 18 usage characteristics that we examined, the five most important were: the number of application executed, whether the machines was pre-installed by the original equipment manufacturer, two sub-populations (two language\/geographic locales), and whether Windows was 64-bit (not 32-bit). We found each of these usage characteristics to differ between beta and post-release, and by adjusting for the differences, accuracy of field quality predictions for Windows improved by ~59%.<\/p>\n","protected":false},"excerpt":{"rendered":"
Many software producers utilize beta programs to predict post-release quality and to ensure that their products meet quality expectations of users. Prior work indicates that software producers need to adjust predictions to account for usage environments and usage scenarios differences between beta populations and post-release populations. 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