Interdependency Aware Qubit and Brownboost Rank Requirement Learning for Large Scale Software Requirement Prioritization

Abstract

Requirements Prioritization (RP) is very indispensable and laborious phase in the course of requirement management of software engineering. Numerous research works have been conducted in the prioritization of small size requirements. However, problems are said to occur while considering large set software project requirements. In order to address the issue, in this paper we present the novel method called the Interdependency-aware Qubit and BrownRoost Rank (IQ-BR) method to prioritize the huge number of requirements. Optimization is a model that identifies the optimal requirements from a set of probable functions with respect to their attributes or requirements. Quantum Optimization is the familiar optimization algorithms is used in the IQ-BR. The novelty of the work lies in the use of the Interdependency-aware Qubit Requirement Selection algorithm and BrownBoost Rank Requirement Prioritization Learning model. An Interdependency-aware Qubit Requirement Selection algorithm is used to address the requirements prioritization issues to handle volatile and interdependencies among requirements during RP. With the optimal requirement selection results, BrownBoost Rank Requirement Prioritization Learning is finally applied to rank the requirements based on the BrownBoost Rank function. The proposed IQ-BR and existing methods are discussed with different factors such as requirement prioritization accuracy, requirement prioritization time, true positive rate and false-positive rate with respect to different functional and non-functional requirements. The observed results show superior performance of our proposed IQ-BR method when compared to state-of-the-art methods.