Combining regression and estimation by analogy in a semi-parametric model for software cost estimation

ABSTRACT

Software Cost Estimation is the task of predicting the effort or productivity required to complete a software project. Two of the most known techniques appeared in literature so far are Regression Analysis and Estimation by Analogy. The results of the empirical studies show the lack of convergence in choosing the best prediction technique between the parametric Regression Analysis and the non-parametric Estimation by Analogy models. In this paper, we introduce the use of a semi-parametric model that achieves to incorporate some parametric information into a non-parametric model combining in this way regression and analogy. Furthermore, we demonstrate the procedure of building such a model on two well-known datasets and we present the comparative results based on the predictive accuracy of the new technique using several accuracy measures. We also perform statistical tests on the residuals in order to assess the improvement in the predictions attained through the new semi-parametric model in comparison to the accuracy of Regression Analysis and Estimation by Analogy when applied separately. Our results show that the semi-parametric model provides more accurate predictions than each one of the parametric and non-parametric approaches.

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.9 Management
          Subjects: Cost estimation

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.9 Management
          Subjects: Productivity

General Terms:
Algorithms, Experimentation, Management, Measurement

Keywords:
estimation by analogy, least squares regression, semi-parametric model, software cost estimation