Abstract: To address the two tightly coupled sub-problems of user story selection and task allocation in agile software development, while considering the uncertainties of new user stories and developers' working hours, a dynamic periodic scheduling model for agile software projects is constructed. A particle swarm optimization algorithm based on grouped learning using both target values and potential values as indicators is proposed. By selecting different learning objects based on the characteristics of diffe- rent groups, the diversity of search is enhanced. Initialization and local search strategies are designed based on return on investment and time utilization, allowing the algorithm to adapt to environmental changes and improve its exploration capabilities. Compared with seven existing algorithms, the proposed algorithm can devise a scheduling plan with greater output value and higher time utilization.

Key words: agile development, software project scheduling, dual-index, group learning, particle swarm optimization