Bibliographische Detailangaben
| Titel: |
DEVELOPMENT AND TESTING OF ALGORITHM FOR AUTOMATING TEAM FORMATION IN IT PROJECTS. |
| Autoren: |
Zolotukha, Roman, Glazunova, Olena |
| Quelle: |
Scientific Journal of Polonia University; 2023, Vol. 61 Issue 6, p203-210, 8p |
| Schlagwörter: |
AUTOMATION, INFORMATION technology projects, MATHEMATICAL models, SOFT skills, EMPLOYEE selection |
| Abstract: |
The research aims to investigate the automation of recruiting and the formation of adaptive teams for IT projects using a proposed mathematical model. Additionally, the proposed algorithm needs to be tested in practice. The algorithm is based on weighting coefficients formed on the basis of expert assessments of key characteristics of candidates which are obtained by testing candidates for soft and hard skills. The research compares the results of two approaches: arbitrary team formation and a method based on a proposed mathematical algorithm. The authors conducted a practical study of both approaches during a hackathon whose task was to develop a web page for a future HR platform. The research methodology involved dividing the participants into two groups. The participants are divided into two groups. The first group forms teams independently during the hackathon, while the second group undergoes soft and hard skills tests during registration to determine team formation using a mathematical algorithm. The study's authors present the effectiveness results of both groups and describe the advantages and possibilities for improving the algorithm. The conclusions drawn identify the achievements of the study and prospects for further development of the algorithm for automating team selection in the IT industry. This work contributes significantly to the understanding of effective strategies for forming IT teams, which is crucial in the face of constant technological changes. [ABSTRACT FROM AUTHOR] |
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Copyright of Scientific Journal of Polonia University is the property of Polonia University of Czestochowa and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Datenbank: |
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