Coevolutionary scheduling of dynamic software project considering the new skill learning

In the process of software project development, completing tasks may require new skills that employees have not yet mastered due to factors such as requirement changes. However, existing studies on software project scheduling usually overlook such new skill demands. This paper designs the learning mechanism targeting the treatment of new skills for project employees, including how to select appropriate employees to learn new skills, the growth curves of new skill proficiencies and the adaptive dedication changes for the selected employees. Three common dynamic events are considered to establish a mathematical model for the dynamic software project scheduling problem considering the new skill learning. To solve the model, a multi-population coevolutionary algorithm-based predictive-reactive scheduling method is proposed in this paper. Three novel strategies are incorporated, which include a response mechanism to environmental changes, a population grouping strategy based on dual indicators, and a dynamic allocation of subpopulation size according to the variation trend of contribution. Systematic experimental results based on ten synthetic instances and three real-world instances show that when dynamic events occur, the proposed algorithm can quickly reschedule the tasks with a better duration, cost and stability compared with six state-of-the-art algorithms, helping project manager make a more informed decision.