Optimal Selection of Software Requirements Using Fuzzy Inference System with Customer Dissatisfaction Constraint

Abstract

One of the challenges for software developers in incremental development methodologies is the issue of the next release problem. In this article, for the first time, this problem will be solved with three goals namely cost, satisfaction, and dissatisfaction. It also has several types of constraints. These three conflicting goals and different constraints make it extremely difficult to choose the optimal subset for product manufacturers. Wrong choices will lead to increased customer dissatisfaction and reduced product sales. To help make smart and error-free choices, we introduce two algorithms. These algorithms determine the merit of the requirements using the fuzzy inference system and then make the choices based on the type of threshold value constraint. We also introduce and implement the constraint of the impact of dissatisfaction on requirements. To conduct the experiments, the proposed algorithm has been compared with the algorithms FMACRO, IBPSO and Kmeans. In all experiments, the solutions resulted from the proposed algorithms had higher quality than the other three algorithms. These results show that choosing the proposed algorithms can provide more satisfaction to customers, reducing their dissatisfaction and lowering product prices. The most important advantages of the proposed algorithms are its scalability and linear order time complexity.