Integrating non-parametric models with linear components for producing software cost estimations

• Semi-parametric models (SPMs) are introduced and applied to software cost estimation (SCE).
• SPMs combine linear regression and well-known non-parametric models (e.g., ANN, SVM).
• A multiple comparisons statistical methodology is used for assessment.
• SPMs along with non-parametric models and linear least squares regression are ranked and clustered according to their accuracy.
• Results show that the use of SPMs is beneficial especially when mixed types of relations exist between cost and various predictors.

A long-lasting endeavor in the area of software project management is minimizing the risks caused by under- or over-estimations of the overall effort required to build new software systems. Deciding which method to use for achieving accurate cost estimations among the many methods proposed in the relevant literature is a significant issue for project managers. This paper investigates whether it is possible to improve the accuracy of estimations produced by popular non-parametric techniques by coupling them with a linear component, thus producing a new set of techniques called semi-parametric models (SPMs). The non-parametric models examined in this work include estimation by analogy (EbA), artificial neural networks (ANN), support vector machines (SVM) and locally weighted regression (LOESS). Our experimentation shows that the estimation ability of SPMs is superior to their non-parametric counterparts, especially in cases where both a linear and non-linear relationship exists between software effort and the related cost drivers. The proposed approach is empirically validated through a statistical framework which uses multiple comparisons to rank and cluster the models examined in non-overlapping groups performing significantly different.