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JOURNALS // Proceedings of the Institute for System Programming of the RAS // Archive

Proceedings of ISP RAS, 2024 Volume 36, Issue 3, Pages 213–224 (Mi tisp898)

This article is cited in 1 paper

Development of an algorithm of the formation of IT project teams based on data from the digital footprint of students

A. V. Melnikova, M. S. Vorobeva, E. V. Egorova, E. D. Chekanova

Tyumen State University

Abstract: The article discusses the development of an algorithm for the formation of IT project teams. The materials were data from the digital footprint of IT students. The student's digital footprint is a constantly updated set of data, including accounting documents of project disciplines, intermediate results in disciplines, and practical training. The paper provides an example of solving the problem of forming teams using graphs. An algorithm based on a graph model has been proposed, which allows you to build a graph reflecting the interaction of students in past projects. Commands are formed based on the constructed graph. Two approaches to command formation are proposed inside the graph model: based on vertex clustering and using graph traversal. To determine the best team, a graph of student communication is built with text tags representing technologies, programming languages, frameworks, etc. The algorithm was tested on data from students of the IT department of Mathematical Support and Administration of Information Systems of the School of Computer Science and requirements for a real project and tested on the spontaneous distribution of students on projects within the discipline. Using the algorithm, you can estimate how successful the split was. The creation of effective teams plays a key role in the successful implementation of projects, therefore, the proposed algorithm can be useful for teachers and project managers in the IT field. The developed algorithm is planned to be integrated into the IT project executors search web service.

Keywords: team formation, digital footprint, skills, students, project, performers, team, graphs, spectral clustering, depth-first search

DOI: 10.15514/ISPRAS-2024-36(3)-15



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