ARP–GWO: an efficient approach for prioritization of risks in agile software development

Abstract

Risk management is considered as a critical project management activity that needs to be performed for successful software development. Within risk management, risk prioritization is an important process which helps the software team to effectively manage the risks at early stage of the project. In agile-based software environment, it is necessary to prioritize the risks in an effective manner in order to address the risks in shorter duration of time. In recent times, swarm intelligence techniques are widely popular in solving various optimization problems in software development process. The main reason is due to its convergence accuracy toward global optimal solution and faster computational time. In this study, an efficient risk prioritization technique termed as ARP–GWO (agile risk prioritization–grey wolf optimization) has been proposed for prioritizing the risk factors present in the agile software development using grey wolf optimization (GWO). The proposed ARP–GWO method helps the organization to mitigate the risks and ensures successful delivery of software products with good quality, in lesser cost and time. The effectiveness of ARP–GWO is analyzed using two performance metrics, namely Index of Integration and Usability Goals Achievement Metric, for which case studies are performed on five industrial projects from two different organizations. The experimental results indicate that ARP–GWO is most effective in prioritization of risks and offers better enhancement with high degree of satisfaction among developers and users as compared with the existing agile process.