Efficient Tuning of COCOMO Model Cost Drivers Through Generalized Reduced Gradient (GRG) Nonlinear Optimization with Best-Fit Analysis

The software effort estimation phase is especially critical in the software development phase. This phase is principally oriented on manipulation of the values of the cost drivers and scale factors. Also, most of the models depend on the size of the project, and a diminutive alteration in the size directs to the in proportion alterations in the effort. Miscalculations of the cost drivers have even additional ear-splitting data as a result too. In this paper, the approach of generalized reduced gradient (GRG) nonlinear optimization with best-fit analysis has been applied to tune the COCOMO model cost drivers so that level of accuracy can be achieved. This proposed methodology has been observed more efficiently in providing the software effort estimation through the help of minimizing MRE value. We have applied this methodology on NASA 63 data sets. We have shown the comparison between the estimated MRE and actual MRE of the data sets. We have also exposed the evaluation between the estimated MMRE and actual MMRE.

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