As can be seen in Table 4., the summary for calculation of MMRE on chosen PF.
that the MMRE reached by PF 20 is not as good as for number 15.
The number 20 shows the worst results of MMRE error.
Summary table for calculation of MMRE on chosen PF 20 15.5 15 Fit PF MMRE [%] MMRE [%] MMRE [%] MMRE [%] Dataset1 78 50 48 43 Dataset2 43 15 14 16 Dataset3 96 70 67 69
In submodel 3, the list with the difference of influenced MMRE and original MMRE is sorted in the descending order of the difference to provide the significant occurrence of the driver.
In submodel 4 we will try to minimize the MMRE by updating the value of cost driver with the help of genetic algorithm in the order of their significance.
As a result, researchers have proposed the most widely used evaluation criterion to assess the performance of software prediction models, that is, the mean magnitude of relative error (MMRE), to evaluate the opulence of prediction systems.
Performance of estimation methods is usually evaluated by several ratio measurements of accuracy metrics including RE (relative error), MRE (magnitude of relative error), and MMRE (mean magnitude of relative error) which are computed as follows:
Decreasing of MMRE and increasing of PRED are the main aim of all estimation techniques.
Significance of 15 cost drivers can be shown by their impact on MMRE of efforts on original 63 NASA datasets.
The occurrence of each cost driver is having linearity with the MMRE calculated between actual efforts produced and estimated effort with COCOMO.