Improving Efficiency of Fuzzy Models for Effort Estimation by Cascading & Clustering Techniques

Abstract

The main challenge in software industry is the process of estimating the cost required for the development or maintenance of a project. Various models have been proposed for constructing a relationship between size of software and its development effort. Various algorithmic cost estimation models exists with their own pros and cons for estimation. Now a days, attention has turned towards Machine learning techniques as few of the problems associated with previous models are being addressed by the soft computing techniques. But, the need for accurate effort estimation in software project management is still a challenge. The literature shows the usage of Fuzzy Logic Controller for Software Effort Estimation, but the computational time is very high as the rulebase is large. The main aim is to reduce the rulebase and improve the efficiency by cascading of Fuzzy Logic Controllers. A case study on NASA 93 dataset is taken for this purpose. The Proposed work is carried out by cascading the Fuzzy Logic Controllers in a stage of two and six for Software Effort Estimation. By increasing the cascading of Fuzzy Logic Controllers, the efficiency has been improved with the minimized size in rulebase. The limitation to this process is to find out the correct number of cascaded Fuzzy Logic Controllers. To overcome this a Fuzzy Model using Subtractive Clustering has been proposed. The rulebase of the models developed by using Subtractive Clustering is further reduced. Considering the rule minimization and the various criteria for assessment, Fuzzy Models developed using Subtractive Clustering provides better software development effort estimates.