Modelling fault-proneness statistically over a sequence of releases: a case study

Many of today’s systems evolve through a series of releases that add new functionality and features, in addition to corrective maintenance. As the systems evolve over time it is necessary to keep track of and manage their problematic components. Our focus is to track system evolution and to react before the systems become difficult to manage. We apply a method based on a collection of statistical methods to perform the tracking. The method is applied in a case study covering four releases of a system consisting of 130 components. In each release components are classified as fault-prone if the number of defect reports written against them are above a certain threshold. The outcome from the study indicates stabilising principal components over the releases and classification trees with lower thresholds in the decision nodes. Also, the variables used in the classification trees’ decision nodes are related to changes in the same files. The discriminant functions used more variables than the classification trees and were more difficult to interpret. Box plots highlight the findings from the other analyses. The results show that PCA together with classification trees are good descriptors of software evolution due to their ability to visualise.