Software requirements selection using Quantum-inspired Elitist Multi-objective Evolutionary algorithm

This paper proposes a Quantum-inspired Elitist Multi-objective Evolutionary algorithm (QEMEA) for software requirements selection, a problem in search based software engineering. Most often software product developments are iterative and incremental in their nature, due to the changes in the customer requirements from time to time. It is a challenging task to select the requirements from a large number of candidates, for the accomplishment of the business goals. The problem is to identify a set of requirements to be included in the next release of the product, by minimizing the cost and maximizing the customer satisfaction. Since minimizing the total cost and maximizing the customer satisfaction are contradictory objectives, the problem has a multi-objective nature. This problem is NP-hard in its nature and so it cannot be solved efficiently by using traditional optimization techniques for large problem instances. QEMEA combines the best features of Evolutionary Algorithms and Quantum Computing. It employs the concepts of Quantum computing such as superposition and interference. The features of QEMEA help in achieving quality pareto-optimal front solutions and faster convergence while using a small population size. The performance of QEMEA is tested on six benchmark problems derived from the literature. The obtained results indicates consistent and superior performance of the algorithm.