Abstract
Imprecise estimation of software development cost is one of the major factors that contributes in the failure of software projects. Several algorithmic models have been devised for cost estimation; but they lack the ability to handle imprecision and uncertainties associated with the software project attributes. Embedding a fuzzy component in the algorithmic model enables it to deal with the imprecision and uncertainty problem; consequently improves its accuracy. However, the performance of any fuzzy system depends on the settings of its parameters. This paper proposes a genetic fuzzy model for effort estimation. Genetic algorithm is used in tuning the fuzzy sets of the model to optimize the estimation accuracy. MATLAB 2012 was used in implementing the proposed model. The model was evaluated using artificial datasets derived from COCOMONASA2 dataset. The experimental results showed that the accuracy and sensitivity of the proposed model is superior to COCOMO. It's note worthy to mention that the idea of the paper is not restricted to COCOMO; it could be applied to other algorithmic models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
