SCRUM

This paper presents a method for automati- cally generating security-oriented test cases from textual requirements in SCRUM environments using Natural Language Processing. The proposed approach has com- bined transformer-based semantic analysis with behavior- driven development test templates to extract and translate functional, non-functional, and misuse-case security requirements. The solution has been tested on 30 real- world requirements derived from agile software projects.

The study substantiates the feasibility of using machine learning technology to improve the iteration planning process in IT projects implemented using the Scrum methodology. The problem of productivity planning in teams is set. The subject and object of the research are formulated. The expected scientific novelty and practical significance of the research results are described. A range of potential issues related to task planning in IT projects, particularly the accuracy of team productivity forecasting, is considered.

The paper analyzes the key aspects of the use of Agile methodologies by distributed project teams in the IT industry with an emphasis on the integration of information and communication systems for effective work in dynamic environments. The challenges faced by the virtual IT industry in the context of the COVID pandemic, the large-scale invasion of Russia, which generated radical changes in the approaches to the management of project teams, have been considered.