(untitled) (Computer systems and information technologies, 2023)
The paper is devoted to improving the allocation and evaluation of tasks in software development. Applied aspects of the development of a task allocation and evaluation system are considered in the process of developing software for further analysis, which ensures the most accurate determination of the person who should perform the task and the corresponding task classification tags based on its description. The proposed system provides accurate and fast identification of a person and a group of tags based on the task description. The main goal of the work is to provide an overview of the current state of the art in this field, the advantages and disadvantages of existing approaches, and to propose improvements to the solution. Challenges related to task allocation and estimation in software development include the need for accurate task estimation, the difficulty of ensuring quality control, and the need for effective communication between developers. To this end, an analysis of the current state of task allocation and estimation was conducted, and a variety of tools and methods available for task allocation and estimation were reviewed, including task tracking systems, project management software, and automated testing tools. Also covered are the various methods used to evaluate tasks, such as peer review, code review, and automated testing. The future of task allocation and estimation in software development is explored, including the potential for further automation and the need for improved communication between developers, as well as the potential for using artificial intelligence to improve task allocation and estimation. Methods used to measure the efficiency of task allocation and evaluation are also discussed, such as time tracking, percentage of tasks completed, and percentage of defects. The paper proposes AI-based approaches such as natural language processing, machine learning, and deep learning.