Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm

Abstract

Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm.