An ensemble of computational intelligence models for software maintenance effort prediction
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Object-oriented class maintainability prediction using internal quality attributes
Information and Software Technology
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There is an increasing interest in more accurate prediction of software maintainability in order to better manage and control software maintenance. Recently, TreeNet has been proposed as a novel advance in data mining that extends and improves the CART (classification and regression trees) model using stochastic gradient boosting. This paper empirically investigates whether the TreeNet model yields improved prediction accuracy over the recently published object-oriented software maintainability prediction models: multivariate adaptive regression splines, multivariate linear regression, support vector regression, artificial neural network, and regression tree. The results indicate that improved, or at least competitive, prediction accuracy has been achieved when applying the TreeNet model.