Static analysis tools as early indicators of pre-release defect density

TLDR

Early estimates of defect density during software development help identify fault‑prone code areas that require additional testing. The study presents an empirical method to predict pre‑release defect density using defects identified by static analysis tools. Defects detected by two static analysis tools were used to fit a model that predicts the actual pre‑release defect density for Windows Server 2003. The results show a strong positive correlation between static‑analysis defect density and actual pre‑release defect density, with predictions highly correlated to observed values and discriminant analysis achieving an 82.91 % classification rate between high‑ and low‑quality components.

Abstract

During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.