Black Hole Algorithm for Software Requirements Prioritization

With the increase in complexity in the software development process, the optimization of requirements management has developed into a critical and necessary task in Software Engineering. The selection and prioritization of software requirements is one of the most commonly encountered issues among the many requirements for software release. Software engineers have introduced several methods for solving these problems. The Black Hole Algorithm (BHA) is a population-based approach. It is among one of the many modern approaches and has been successfully applied to solve optimization problems. The purpose of this study is to provide a BHA-based solution to the Requirements Prioritization (RP) problem. Furthermore, the proposed BHA-based solution was evaluated on three real-world datasets (RALIC, Word, and ReleasePlanner), and its performance was compared with that of multiple state-of-the-art algorithms, including Ant Colony Optimization (ACO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Fitness Dependent Optimizer (FDO), Goose Algorithm (GAO), and Lagrange Elementary Optimization (LEO). The findings show that BHA consistently yielded higher fitness values, reaching 98.37% for Word, 98.82% for ReleasePlanner, and 99.67% for RALIC. In contrast, the highest percentages achieved by PSO were 95.01%, 90.53%, and 94.37%, respectively, while ACO, GA, GWO, FDO, GAO, and LEO also remained behind BHA in all three datasets. Thus, BHA outperforms competing techniques and provides a better solution to the problem of software requirements prioritization.