Application of multivariate analysis for software fault prediction

Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. The primary goal of our research was to develop and refine techniques for early prediction of fault-prone modules. The objective of this paper is to review and improve an approach previously examined in the literature for building prediction models, i.e. principal component analysis (PCA) and discriminant analysis (DA). We present findings of an empirical study at Ericsson Telecom AB for which the previous approach was found inadequate for predicting the most fault-prone modules using software design metrics. Instead of dividing modules into fault-prone and not-fault-prone, modules are categorized into several groups according to the ordered number of faults. It is shown that the first discriminant coordinates (DC) statistically increase with the ordering of modules, thus improving prediction and prioritization efforts. The authors also experienced problems with the smoothing parameter as used previously for DA. To correct this problem and further improve predictability, separate estimation of the smoothing parameter is shown to be required.