METHOD FOR DEVELOPMENT MODELS OF POLYSUBJECT MULTIFACTOR ENVIRONMENT OF SOFTWARE COMPLEX’S SUPPORT

Authors

  • A. I Pukach Lviv Polytechnic National University, Lviv, Ukraine, Ukraine
  • V. M Teslyuk Lviv Polytechnic National University,Lviv,Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2025-2-19

Keywords:

software product, complex support, software product support environment, impact factor, automation, artificial neural networks, multilayer perceptron

Abstract

Context. The task of development the models of a polysubject multifactor environment for software’s complex support is considered in this research, that ensures possibilities of taking into account the influence of various impact factors onto the supported software complexes themselves, onto their complex support’s processes, as well as onto the subjects (interacting with them) who provide and implement this complex support. The object of study are the processes of complex support of software products, the processes of automation of this complex support, the processes of influence of impact factors onto the object and subjects of the complex support of software products, as well as the processes of perception’s subjectivization of the supported object by relevant subjects of interaction with it. The subject of study are methods and means of artificial neural networks, in particular a multilayer perceptron, as well as a computer design and modeling. Objective is the development of the method for building models of a polysubject multifactor environment(s) of the complex support of software product(s).
Method. The developed method for building models of a polysubject multifactor environment of software complexes’ support is proposed, which makes it possible (in an automated mode) to obtain appropriate models, based on which, later on – to investigate the strengths and weaknesses of a specific researched complex support’s environment(s) of a particular investigated software product(s), in order to ensure further improvement and automation of this complex support based on the study and analysis of impact factors, which form the subjective vision and perception of this complex support by those subjects who directly provide and perform it, that is, in fact, on whom this support itself depends, as well as its corresponding qualitative and quantitative characteristics and indicators.
Results. The results of functioning of the developed method are corresponding models of investigated polysubject multifactor environments of the complex support of software products, which take into account the presence and the level of influence of relevant existing and present factors performing impact onto the subjects of interaction with supported software complexes, which (subjects) directly provide and perform the complex support for the studied software products, and also form relevant researched support environments. In addition, as an example of a practical application and approbation, the developed method was used, in particular, to solve the applied practical task of determining the dominant and the deficient factors of influence of a polysubject multifactor environment of the studied software complex’s support, with presenting and analyzing the obtained results of resolving the given task.
Conclusions. The developed method resolves the problem of building models of a polysubject multifactor environment of the complex support of software products, and ensures taking into account the action of various impact factors performing influence onto the supported software complex itself, onto the processes of its support, as well as onto the subjects of interaction with it, which (subjects) provide and perform this complex support. In particular, the developed method provides possibilities for modeling and investigating a polysubject multifactor environments of the “in focus” software product’s complex support, which reflect the global (or the local, it depends on the specific tasks) impact of various existing factors making influence onto the object of support itself (the supported software complex, or the processes of its complex support), as well as onto the subjects which directly carry out and implement this complex support in all its possible and/or declared manifestations. The practical approbation of the developed method has been carried out by solving specific applied practical tasks, one of which is presented, as an example, in this paper, – which is the task of determining the dominant and the deficient factors of influence of a polysubject multifactor environment of the studied software complex’s support, and this approbation, actually, confirms its effectiveness in solving a stack of applied practical problems related to researching the impact of factors performing influence onto the complex support of software products, using the advantages of artificial intelligence technologies, machine learning, artificial neural networks, and multilayer perceptron in particular

Author Biographies

A. I Pukach, Lviv Polytechnic National University, Lviv, Ukraine

PhD, Аssistant of ACS Department, Institute of Computer Sciences and Informational Technologies

V. M Teslyuk, Lviv Polytechnic National University,Lviv,Ukraine

Doctor of Sciences, Professor, Head of ACS Department, Institute of Computer Sciences and Informational Technologies

References

Panwar A., Peddi P. Implementation of Software Testing Using Machine Learning: A Systematic Mapping Study, JJTU Journal of Renewable Energy Exchange, 2023, Vol. 11, Iss. 7, pp. 58–64. – DOI: 10.58443/ijrex.11.7.2023.58-64

Gadani N.N. Artificial Intelligence: Leveraging Ai-Based Techniques For Software Quality, International Research Journal of Modernization in Engineering Technology and Science, 2024, Vol. 06, Iss. 07, pp. 757–769. – DOI: 10.56726/IRJMETS60018

Chaudhary J., Anand P. Predictive Modeling Of Automation In Software Testing A Machine Learning Approach For Efficient Test Case Selection [Electronic resource], Advances in Mechanics, 2023, Vol. 11, Iss. 2, pp. 128–139. Mode of access: https://www.advancesinmechanics.com/pdf/2023-128.pdf (date of access: 01.01.2025). – Title from screen.

Manchana R. The DevOps Automation Imperative: Enhancing Software Lifecycle Efficiency and Collaboration, European Journal of Advances in Engineering and Technology, 2021, Vol. 8, Iss. 7, pp. 100–112. DOI: 10.5281/zenodo.13789734

Ogala J. O. A Complete Guide to DevOps Best Practices, International Journal of Computer Science and Information Security (IJCSIS), 2022, Vol. 20, No. 2, pp. 1–6. DOI: 10.5281/zenodo.6376787

Adnan S., Moin K., Imran J. DevOps with Agile: Best practices to improve software quality, International Conference On Biological Research And Applied Science, 2023, pp. 61– 66. DOI: 10.37962/ibras/2023/61-66

Rajadhyax D. A Survey of Methods, Tools and Applications of Knowledge Base Construction (KBC) [Electronic resource], Telecom Business Review: SIDTM Journal, 2020, Vol. 13, Iss. 1, pp. 20–26. Mode of access:

https://sidtm.edu.in/wpcontent/uploads/2021/06/tbr2020.pdf#page=25 (date of access: 01.01.2025). – Title from screen.

Verrev M. Combining Semantic Parsing Frameworks for Automated Knowledge Base Construction [Electronic resource], 6th Workshop on Advances In Argumentation In Artificial Intelligence, 2022, 11 pages. Mode of access: https://ceur-ws.org/Vol-3354/paper1.pdf (date of access: 01.01.2025). – Title from screen.

Rapp A., Curti L., Boldi A. The human side of humanchatbot interaction: A systematic literature review of ten years of research on text-based chatbots, International Journal of Human-Computer Studies, 2021, Vol. 151, Iss. 3, Article ID: 102630. DOI: 10.1016/j.ijhcs.2021.102630

Ahmed S., Singh M., Doherty B. et al. AI for Information Technology Operation (AIOps): A Review of IT Incident Risk Prediction, 9th International Conference on Soft Computing and Machine Intelligence (ISCMI), 2022, pp. 253–257. DOI: 10.1109/ISCMI56532.2022.10068482

Paramesh S. P., Shreedhara K.S. A deep learning based IT service desk ticket classifier using CNN [Electronic resource], ICTACT journal on soft computing, 2022, Vol. 13, Iss. 1, pp. 2805–2812. Mode of access: https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_9_ 2805_2812.pdf (acc. date: 02.01.2025). – Title from screen.

Pukach A.I. Mathematical model for analysis of influencing factors on software complexes support / A.I. Pukach, V.M. Teslyuk // Printing and Publishing. – 2024. – 1(87). – P. 75–85. – DOI: 10.32403/0554-4866-2024-1-87-75-85

Pukach A. I., Teslyuk V. M. Model of decomposed insulating dominance for the analysis of influencing factors of software complexes support automation, Scientific Bulletin of UNFU, 2024, 34(5), pp. 170–179. – DOI: 10.36930/40340521

Pukach A. I., Teslyuk V. M. Information model for automation of software complexes support influencing factors analysis with usage of the R-system and Python environments, Bulletin of Lviv State University of Life Safety, 2024, 29, pp. 54–64. – DOI: 10.32447/20784643.29.2024.06

Rasool N., Yousaf S., Haseeb U. et al. Scrum and the Agile procedure’s impact on software project management, Journal of Jilin University (Engineering and Technology Edition), 2023, Vol. 42, Iss. 2, pp. 380–392. – DOI: 10.17605/OSF.IO/MQW9P

Thant K. S., Tin H. H. K.The impact of manual and automatic testing on software testing efficiency and effectiveness [Electronic resource], Indian Journal of Science and Research, 2023, Vol. 3, Iss. 3, pp. 88–93. Mode of access: https://www.ijsronline.org/issue/20230423-144118.016.pdf (acc. date: 02.01.2025). Title from screen.

Mehmood A. M., Murtuza A. M., Vazeer A. M. Impact of artificial intelligence on the automation of digital health system, International Journal of Software Engineering & Applications (IJSEA), 2022, Vol. 13, No. 6, pp. 23–29. DOI: 10.5121/ijsea.2022.13602

Downloads

Published

2025-06-29

How to Cite

Pukach, A. I., & Teslyuk, V. M. (2025). METHOD FOR DEVELOPMENT MODELS OF POLYSUBJECT MULTIFACTOR ENVIRONMENT OF SOFTWARE COMPLEX’S SUPPORT. Radio Electronics, Computer Science, Control, (2), 217–231. https://doi.org/10.15588/1607-3274-2025-2-19

Issue

Section

Progressive information technologies