system, intelligent system, system design, model, neural networks, machine learning
Abstract
This article presents a model of intelligent task distribution for a development team, built on the basis of neural networks. Information about the available skills, current employment and previous performance of individual team members are automatically taken into account when making decisions about assigning new tasks. The architecture of the intelligent system is modular, for the client part of the task distribution system built on the basis of the proposed model, it is proposed to use ASP.Core, for machine learning – Python API. The neural model was trained on historical data on the performance of tasks in development teams, as well as project requirements. The results obtained confirm that the proposed model significantly increases the efficiency of task distribution compared to the traditional manual approach. The model allows for a more even distribution of the workload among individual team members and minimizes the effort spent on developing team solutions. Team productivity increases due to an increase in the speed of task execution, a decrease in the overload of individual developers, taking into account the competence and previous experience of performers, improving deadline forecasting, reducing the risk of delays or failure to complete assigned tasks.
Author Biographies
V. S. Vorobiov, Taras Shevchenko National University of Kyiv, Kyiv
Senior Student
L. V Zubyk, National University of Water and Environmental Engineering, Rivne
Associate Professor
Ya. Ya. Zubyk, National University of Water and Environmental Engineering, Rivne
