Tempest: Towards early identification of failure-prone binaries

Early estimates of failure-proneness can be used to help inform decisions on testing, refactoring, design rework etc. Often such early estimates are based on code metrics like churn and complexity. But such estimates of software quality rarely make their way into a mainstream tool and find industrial deployment. In this paper we discuss about the Tempest tool that uses statistical failure-proneness models based on code complexity and churn metrics across the Microsoft Windows code base to identify failure-prone binaries early in the development process. We also present the tool architecture and its usage as of date at Microsoft.