Volume 4 Number 5 (Jul. 2009)
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JSW 2009 Vol.4(5): 452-459 ISSN: 1796-217X
doi: 10.4304//jsw.4.5.452-459

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

Ch. Satyananda Reddy, KVSVN Raju
DENSE Research Group,Department of Computer Science and Systems Engineering,College of Engineering, Andhra University, Visakhapatnam, INDIA

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.

Index Terms—COCOMO, Fuzzy based effort estimation, Gaussian membership function, Software cost estimation, Software effort estimation and Project management.

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Cite: Ch. Satyananda Reddy, KVSVN Raju, "An Improved Fuzzy Approach for COCOMO’s Effort Estimation using Gaussian Membership Function," Journal of Software vol. 4, no. 5, pp. 452-459, 2009.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jsw@iap.org
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