Optimization of Interval Type-2 Fuzzy Logic System for Software Reliability Prediction

Abstract

Since real world application is fraught with high amount of uncertainty, such as applicable to software reliability, there should be a method of handling the uncertainty. This paper presents a model to properly handle uncertainty in software data for effective prediction of the reliability of software at the early phase of software development process. In this paper we employed a Takagi-Sugeno-Kang (TSK)-based interval type 2 fuzzy logic systems with artificial neural network learning for the prediction of software reliability. The degree of membership grades of the interval type 2 fuzzy sets (IT2FSs) are obtained using interval type Gaussian membership function with fixed mean and uncertain standard deviation. The parameters of the IT2FLS membership functions are optimized using gradient descent (GD) back-propagation algorithm. As inputs to the system, reliability relevant software requirement metrics and the software size metrics are used. The proposed new approach makes use of qualitative data of requirement metrics of twenty three real software projects to examine its predictive ability. The performance of the model is evaluated using five performance metrics and found to provide better results when compared with existing approaches.