Building Scalable Failure-proneness Models Using Complexity Metrics for Large Scale Software Systems

Building statistical models for estimating failure-proneness of systems can help software organizations make early decisions on the quality of their systems. Such early estimates can be used to help inform decisions on testing, refactoring, code inspections, design rework etc. This paper demonstrates the efficacy of building scalable failure-proneness models based on code complexity metrics across the Microsoft Windows operating system code base. We show the ability of such models to estimate failure-proneness and provide feedback on the complexity metrics to help guide refactoring and the design rework effort.