Classification-tree models of software-quality over multiple releases

This paper presents an empirical study that evaluates software-quality models over several releases, to address the question, How long will a model yield useful predictions? The Classification And Regression Trees (CART) algorithm is introduced. CART can achieve a preferred balance between the two types of misclassification rates. This is desirable because misclassification of fault-prone modules often has much more severe consequences than misclassification of those that are not fault-prone. The case-study developed 2 classification-tree models based on 4 consecutive releases of a very large legacy telecommunication system. Forty-two software product, process, and execution metrics were candidate predictors. Model #1 used measurements of the first release as the training data set; this model had II important predictors. Model #2 used measurements of the second release as the training data set; this model had 15 important predictors, Measurements of subsequent releases were evaluation data sets. Analysis of the models' predictors yielded insights into various software development practices. Both models had accuracy that would be useful to developers. One might suppose that software-quality models lose their value very quickly over successive releases due to evolution of the product and the underlying development processes. We found the models remained useful over all the releases studied.