Use of relative code churn measures to predict system defect density

Software systems evolve over time due to changes in requirements, optimization of code, fixes for security and reliability bugs etc. Code chum, which measures the changes made to a component over a period of time, quantifies the extent of this change. We present a technique for early prediction of system defect density using a set of relative code chum measures that relate the amount of chum to other variables such as component size and the temporal extent of chum. Using statistical regression models, we show that while absolute measures of code chum are poor predictors of defect density, our set of relative measures of code chum is highly predictive of defect density. A case study performed on Windows Server 2003 indicates the validity of the relative code chum measures as early indicators of system defect density. Furthermore, our code chum metric suite is able to discriminate between fault and not fault-prone binaries with an accuracy of 89.0 percent.