Abstract
This paper proposes a new software development effort estimation model. The new model's design is based on the function point analysis, categorical variable segmentation (CVS), and stepwise regression. The stepwise regression method is used for the creation of the unique estimation model of each segment. The estimation accuracy of the proposed model is compared to clustering-based models and the international function point user group model. It is shown that the proposed model increases estimation accuracy when compared to baseline methods: non-clustered functional point analysis and clustering-based models. The new CVS model achieves a significantly higher accuracy than the baseline methods.
Highlights
The Software Engineering industry and research employ mathematical models used to design a Parametric Estimation Model (PEM)
BASELINE MODELS 1) THE IFPUG MODEL The estimated effort in person-hours is compared to known Effort values in the testing data-set
The new categorical variable segmentation (CVS) model was introduced in this study
Summary
The Software Engineering industry and research employ mathematical models used to design a Parametric Estimation Model (PEM). These PEMs are proposed in order to resolve Budgeting, Software Complexity (Size), or Developmenttime Planning [1], [2]. In Software Engineering Development Effort Estimations (SEDEE), a Use Case Points (UCP) [3], or Function Point Analysis (FPA) [4], may be used as a PEM. Improving PEM accuracy is the main aim in software development effort estimation research. During the past several years, research in software development effort has focused on improving the accuracy of the estimations. Improvements are focused on improving or new designs of algorithms – the counting process
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