Applying search algorithms for optimizing stakeholders familiarity and balancing workload in requirements assignment

Abstract

During the early phase of project development lifecycle of large scale cyber-physical systems, a large number of requirements are needed to be assigned to different stakeholders from different organizations or different departments of the same organization for reviewing, clarifying and checking their conformance to industry standards and government or other regulations. These requirements have different characteristics such as various extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of cyber-physical systems, we discovered an optimization problem, where an optimal solution is required for assigning requirements to different stakeholders by maximizing their familiarities to the assigned requirements while balancing the overall workload of each stakeholder. We propose a fitness function which was investigated with four search algorithms: (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Alternating Variable Method, whereas Random Search is used as a comparison base line. We empirically evaluated their performance for finding an optimal solution using a large-scale industrial case study and 120 artificial problems with varying complexity. Results show that (1+1) EA gives the best results together with our proposed fitness function as compared to the other three algorithms.