A comparative study of meta-heuristic optimisation techniques for prioritisation of risks in agile software development

Abstract

Risks are in general termed as threats or uncertainties that influence the project performance and its outcomes to the greater extent. To ensure software quality and project success, every organisation should enforce a proper mechanism to efficiently manage the risks irrespective of the development model they follow. Risk prioritisation is a most critical step in risk management process that helps the organisation to resolve the risks in shorter duration of time. In this paper, a comparative study about different meta-heuristic optimisation techniques for prioritising the risks in agile environments is presented. The five most effective meta-heuristic optimisation algorithms such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Grey Wolf Optimisation (GWO) and Analytical Hierarchy Process (AHP) are considered and the results are evaluated based on four key criterion attributes such as error rate, accuracy, reliability, and running time. The result proves that GWO outperforms other four meta-heuristic optimisation techniques for the prioritisation of risks in agile environment.