Analytic Study of Fuzzy-based Model for Software Cost Estimation

The need for successful software projects has been a major area of discourse amongst researchers and software developers in academia and software industry respectively. Failure of software projects has been tied to flawed estimation at the early stages of software development life cycle. Recently, soft computing techniques such as Fuzzy logic models has been seen as an alternative to handle uncertainties and vagueness of input parameters to the early software estimation models. In order to analyze the various conditions which affect estimation accuracy of fuzzy-based models, a sample of 93 COCOMO NASA projects was used to develop two groups of fuzzy models. One was the controlled group while the other was the experimental group varying in conditions of model structure, linguistic variables, parameters of input and output variables. A comparative analysis of the Mean Magnitude of Relative Error (MMRE) and Prediction accuracy Pred(l) evaluation criteria for the models was made and findings recorded. Results from the experiments show that the performance of a fuzzy-based software cost estimation model utilizing Takagi-Sugeno inference, Gaussian/Sigmoid membership function with more number of input variables and linguistics variables is more efficient. CCS Concepts • Software and its engineering ➝Software system structures ➝Software system models ➝Model-driven software engineering • Computing methodology➝Artificial Intelligence Knowledge representation and reasoning➝Vagueness and fuzzy logic