Improving the Accuracy of Early Software Size Estimation Using Analysis-to-Design Adjustment Factors (ADAFs)

Abstract

Early software size estimation is a challenging task since limited information is available at the time of project inception. Additional information, however, is gradually added as development progresses. The goal of this research is to quantitatively capture the impact on early software size estimation of this additional information introduced especially when transitioning from the analysis phase to the design phase by comparing the analysis class diagram (ACD) and the design class diagram (DCD). We introduce a new class of metrics called analysis-to-design adjustment factors (ADAFs) to accomplish this goal. ADAFs are calculated for four different class diagram metrics – number of classes (NOC), number of attributes (NOA), number of methods (NOM), and number of relationships (NOR) – used in different class diagram-based software size estimation models. We use practical, theoretical, and empirical validation methods to evaluate the applicability of these ADAFs. To assess the utility of these ADAFs in early software size estimation, we compare the accuracy of existing early software size estimation models before and after the application of ADAFs. Results indicate a marked improvement in the accuracy of these models after the application of ADAFs. Furthermore, regression-based models employing problem domain metrics have also been built to predict these ADAFs. All of these models are statistically significant (p-values < 0.05) with R2 values between 0.42 and 0.88.

Keywords

• Analysis-to-design adjustment factors (ADAFs)
• class diagram
• early software size estimation
• empirical validation
• multiple linear regression models