An Improved Fuzzy Approach for COCOMO’s Effort Estimation Using Gaussian Membership Function

Abstract

In software industry Constructive Cost Model (COCOMO) is considered to be the most widely used model for effort estimation. Cost drivers have significant influence on the COCOMO and this research investigates the role of cost drivers in improving the precision of effort estimation. It is important to stress that uncertainty at the input level of the COCOMO yields uncertainty at the output, which leads to gross estimation error in the effort estimation. Fuzzy logic has been applied to the COCOMO using the symmetrical triangles and trapezoidal membership functions to represent the cost drivers. Using Trapezoidal Membership Function (TMF), a few attributes are assigned the maximum degree of compatibility when they should be assigned lower degrees. To overcome the above limitation, in this paper, it is proposed to use Gaussian Membership Function (GMF) for the cost drivers by studying the behavior of COCOMO cost drivers. The present work is based on COCOMO dataset and the experimental part of the study illustrates the approach and compares it with the standard version of the COCOMO. It has been found that Gaussian function is performing better than the trapezoidal function, as it demonstrates a smoother transition in its intervals, and the achieved results were closer to the actual effort.