Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model

Constructive Cost Model II (COCOMO II) is investigated as the most popular model for software cost estimation. COCOMO II depends on several variables or Cost Drivers (CD). This research investigates the role of Effort Multiplier (EM) and Line of Code (LOC) to improve the accuracy of cost estimation. Fuzzy Logic has been implemented to the COCOMO II to represent the EM. Furthermore, in order to produce better estimation, this research uses Gaussian Membership Function to redesign the Effort Multiplier by studying the behavior of COCOMO II. This research also applies Neural Network (NN) approach to increase the accuracy of software effort estimation by training the software development datasets. The result is proposed model gives contribution to decrease error significantly.