%0 Journal Article %1 IJACSA.2011.020515 %A Prasad Reddy P.V.G.D. Sudha K. R, Rama Sree P %D 2011 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Cost Development Drivers; EAF; Effort; Functions; Fuzzy Identification; Membership NASA93 Rules; dataset. %N 5 %T Application of Fuzzy Logic Approach to Software Effort Estimation %U http://ijacsa.thesai.org/ %V 2 %X The most significant activity in software project management is Software development effort prediction. The literature shows several algorithmic cost estimation models such as Boehm’s COCOMO, Albrecht&\#39;s&\#39; Function Point Analysis, Putnam’s SLIM, ESTIMACS etc., but each model do have their own pros and cons in estimating development cost and effort. This is because project data, available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. The need for accurate effort prediction in software project management is an ongoing challenge. A fuzzy model is more apt when the systems are not suitable for analysis by conventional approach or when the available data is uncertain, inaccurate or vague. Fuzzy logic is a convenient way to map an input space to an output space. Fuzzy Logic is based on fuzzy set theory. A fuzzy set is a set without a crisp, clearly defined boundary. It is characterized by a membership function, which associates with each point in the fuzzy set a real number in the interval 0, 1, called degree or grade of membership. The membership functions may be Triangular, GBell, Gauss and Trapezoidal etc. In the present paper, software development effort prediction using Fuzzy Triangular Membership Function and GBell Membership Function is implemented and compared with COCOMO. A case study based on the NASA93 dataset compares the proposed fuzzy model with the Intermediate COCOMO. The results were analyzed using different criterions like VAF, MARE, VARE, MMRE, Prediction and Mean BRE. It is observed that the Fuzzy Logic Model using Triangular Membership Function provided better results than the other models.

@article{IJACSA.2011.020515, abstract = {The most significant activity in software project management is Software development effort prediction. The literature shows several algorithmic cost estimation models such as Boehm’s COCOMO, Albrecht\&\#39;s\&\#39; Function Point Analysis, Putnam’s SLIM, ESTIMACS etc., but each model do have their own pros and cons in estimating development cost and effort. This is because project data, available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. The need for accurate effort prediction in software project management is an ongoing challenge. A fuzzy model is more apt when the systems are not suitable for analysis by conventional approach or when the available data is uncertain, inaccurate or vague. Fuzzy logic is a convenient way to map an input space to an output space. Fuzzy Logic is based on fuzzy set theory. A fuzzy set is a set without a crisp, clearly defined boundary. It is characterized by a membership function, which associates with each point in the fuzzy set a real number in the interval [0, 1], called degree or grade of membership. The membership functions may be Triangular, GBell, Gauss and Trapezoidal etc. In the present paper, software development effort prediction using Fuzzy Triangular Membership Function and GBell Membership Function is implemented and compared with COCOMO. A case study based on the NASA93 dataset compares the proposed fuzzy model with the Intermediate COCOMO. The results were analyzed using different criterions like VAF, MARE, VARE, MMRE, Prediction and Mean BRE. It is observed that the Fuzzy Logic Model using Triangular Membership Function provided better results than the other models. }, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Prasad Reddy P.V.G.D. Sudha K. R}, Rama Sree P}, biburl = {https://www.bibsonomy.org/bibtex/20caf5dc3f0e18fa9b3b14f465562b242/thesaiorg}, interhash = {09444d7467f1b4fcc0bff8a2d2ab0a94}, intrahash = {0caf5dc3f0e18fa9b3b14f465562b242}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Cost Development Drivers; EAF; Effort; Functions; Fuzzy Identification; Membership NASA93 Rules; dataset.}, number = 5, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Application of Fuzzy Logic Approach to Software Effort Estimation}}, url = {http://ijacsa.thesai.org/}, volume = 2, year = 2011 }