Use of relative code churn measures to predict system defect density

TLDR

Software systems evolve over time due to changes in requirements, optimization, and bug fixes, and code churn quantifies the extent of these changes. The study proposes a technique to predict system defect density early by using relative code churn measures that relate churn to component size and time. The approach employs statistical regression models to demonstrate that relative code churn measures outperform absolute churn in predicting defect density. A case study on Windows Server 2003 confirms the predictive power of relative code churn, achieving 89 % accuracy in distinguishing fault‑prone from non‑fault‑prone binaries.

Abstract

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