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  3. Volume 7, Number 2, 2025
  4. Archimedes Spiral-Based Model for Multifactor Positioning of Complex Software Support Object

Archimedes Spiral-Based Model for Multifactor Positioning of Complex Software Support Object

CSN.
2025;
Volume 7, Number 2
: pp. 167 - 176
https://doi.org/10.23939/csn2025.02.167
Authors:
  1. A. I. Pukach
  2. V. M. Teslyuk
1
Lviv Polytechnic National University, Department of Automated Control Systems, Ukraine
2
Lviv Polytechnic National University, Department of Automated Control Systems, Ukraine

The work is devoted to solving the scientific and applied problem  of positioning objects of software’s complex support within the existing polyfactorial multi-subject support environment. An Archimedes spiral-based model has been developed for the multifactor positioning of objects of (software’s complex support), which provides the possibility of solving the declared scientific and applied problem. The developed model increases the determinism level of an irrational component of intersubjective interaction within the range from “1” (for intersubjective interaction environments with already existing high level of an irrational component representativeness) up to the values multiple of “x10” (for environments with a low existing/current level of this irrational component representativeness). A key feature of the developed model is the  interpretation of the evolutionary processes of complex support of the investigated objects (which represent directly the supported software itself, as well as the processes of its complex support), which is represented by the concentric turns of Archimedes spiral. Approbation of the developed model has been carried out on the example of solving a particular practical applied task of monitoring the dominant impact factor of the complex support environment of the investigated multifactorial positioning’s object, with further visualization of the obtained results.

Archimedes spiral
complex support
impact factors
model
multifactor positioning
software
support object
  1. Tatineni, S. (2023). AI-Infused Threat Detection and Incident Response in Cloud Security. International Journal of Science and Research, 12(11), pp. 998–1004. DOI: https://doi.org/10.21275/sr231113063646.
  2. Paramesh, S., & Shreedhara, K. (2022). A Deep Learning Based It Service Desk Ticket Classifier Using Cnn. Online) Ictact Journal on Soft Computing, 13(1), pp. 2805–2812. DOI: https://doi.org/10.21917/ijsc.2022.0399.
  3. Ribeiro, D.P., Anjo, A., & Henriques, P.R. (2022). Design and implementation of a chatbot as a tool to assist a helpdesk team. Proceedings of the International Conferences on Applied Computing and WWW/Internet, pp. 139–147. DOI:    https://doi.org/10.33965/ac_icwi2022_202208l017.
  4. Manchana, R. (2021). The DevOps Automation Imperative: Enhancing Software Lifecycle Efficiency and Collaboration. European  Journal of Advances in Engineering  and Technology, 8(7), pp. 100–112. DOI: https://doi.org/10.5281/zenodo.13789734.
  5. None Suprit Pattanayak, Murthy, N. P., & Mehra, N. A. (2024). Integrating AI into DevOps pipelines: Continuous integration, continuous delivery, and automation in infrastructural management: Projections for future. International Journal of Science and Research Archive, 13(1), pp. 2244–2256. DOI: https://doi.org/ 10.30574/ijsra.2024.13.1.1838.
  6. Hamza, O., Collins ,A., Eweje, A., & Babatunde, G. O. (2023). Agile-DevOps Synergy for Salesforce CRM deployment: Bridging Customer Relationship Management with Network Automation. International Journal of Multi- disciplinary Research and Growth Evaluation, 4(1), pp. 668–681. DOI: https://doi.org/10.54660/.ijmrge.2023.4.1. 668-681.
  7. Jin, Z. (2024). Integrating AI into Agile Workflows: Opportunities and Challenges. Applied and Computational Engineering, 100(1), pp. 49–54. DOI: https://doi.org/10.54254/2755-2721/100/20251754.
  8. De Silva, D., Gunathilake, M., Sooriyabandara, H., Chandrarathna, M., Bandara, S., & Thilakarathna, S. (2023). A Case Study of Test Automation in Agile Software Development. International Journal of Science and Engineering Applications, 12(05), pp. 41–49. DOI: https://doi.org/10.7753/ijsea1205.1013.
  9. Swathi, B., & Tiwari, H. (2021). Test Automation Framework Using Soft Computing Techniques. 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 100(16), pp. 4909–4917. DOI: https://doi.org/10.1109/icaect49130.2021.9392602.
  10. Mishra, L., & Nayak, S. (2024). Advanced test automation techniques for DevOps: Bridging the gap between test-driven development and continuous deployment in agile environments. World Journal of Advanced Research and Reviews, 23(3), pp. 855–867. DOI: https://doi.org/10.30574/wjarr.2024.23.3.2649.
  11. Morimoto, C., & Minami, K. (2023). A Proposal of a New Team Building Method in IT PBL: A Trial of the SENTAI-Hero-Exercise. In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023), 2, pp. 364–370. DOI: https://doi.org/10.5220/0011828600003470.
  12. Dwi Ningsih, A., Ariani, D., Sagala, S., & Harahap, D. (2022). Project Team Bulding, Conflict and Negotiation. Devotion Journal of Community Service, 3(14), pp. 2519–2533. DOI: https://doi.org/10.36418/ dev.v3i14.302.
  13. Burkard, M., & Fontoura, L. (2023). People Management Problems and Practices in Software Development Projects: A Systematic Literature Review. In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023). 2, pp. 179–186. DOI: https://doi.org/10.5220/0011985300003467.
  14. Veeramachaneni, V. (2020). Factors That Contribute To The Success Of A Software Organisation's Devops Environment: A Systematic Review. International Journal for Recent Developments in Science & Technology, 04(11), pp. 5–11. DOI: https://doi.org/10.48550/arXiv.2211.04101.
  15. Gilal, R., Omar, M., & Rejab, M.M. (2023). The key factors contribute to time pressure in software development projects: A review. International Journal of Advanced and Applied Sciences, 10(10), pp. 155–165. DOI: https://doi.org/10.21833/ijaas.2023.10.018.
  16. Zhou, P. (2023). Systematic Literature Review Protocol on Influential Factors for Software Process Improvement in Global Software Development. Global Software Development, pp. 1–17. DOI: https://doi.org/10.13140/RG.2.2.21750.98888.
  17. Pukach, A. I., & Teslyuk, V. M. (2025). Method of forming multifactor portraits of the subjects supporting software complexes, using a multilayer perceptron. Radio Electronics, Computer Science, Control, (1), pp. 130–141. DOI:    https://doi.org/10.15588/1607-3274-2025-1-12.