Розроблення моделі мультифакторного портрету суб’єктів підтримки програмних комплексів з застосуванням штучних нейронних мереж

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dc.citation.epage207
dc.citation.issue2
dc.citation.journalTitleКомп'ютерні системи та мережі
dc.citation.spage197
dc.citation.volume6
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorПукач, А. І.
dc.contributor.authorТеслюк, В. М.
dc.contributor.authorPukach, A.
dc.contributor.authorTeslyuk, V.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-12-11T11:15:19Z
dc.date.created2024-10-10
dc.date.issued2024-10-10
dc.description.abstractРозглянуто фактори впливу, що формують індивідуалістичне сприйняття об’єктів підтримки відповідними суб’єктами, які взаємодіють з ними, напряму або опосеред- ковано. Досліджено та запропоновано форму представлення факторів впливу на підтри- мувані програмні комплекси, що включає набір вхідних характеристик досліджуваного об’єкта підтримки, набір факторів впливу у вигляді матричної функції перетворення та набір вихідних характеристик результуючого сприйняття все того ж досліджуваного об’єкта підтримки, проте в індивідуалістичному сприйнятті кожного окремого суб’єкта взаємодії з ним. Досліджено можливості інкапсуляції штучних нейронних мереж у форму представлення факторів впливу на підтримувані програмні комплекси та запропоновано використання саме багатошарового перцептрона для здійснення відповідних інкапсу- ляцій. Розроблено та представлено відповідну модель мультифакторного портрету суб’єктів підтримки програмних комплексів із застосуванням штучних нейронних мереж, зокрема багатошарового перцептрона. Розв’язано прикладну практичну задачу визначення дефіцитних факторів впливу членів команди підтримки програмного комплексу.
dc.description.abstractImpact factors, that are shaping the individualistic perception of the supported objects by the relevant subjects, who interact with them, directly or indirectly, are considered in this research. A form of impact factors’ (performing impact on the supported software complexes) representation has been studied and proposed. Aforementioned form includes: a set of input characteristics of the researched supported object; a set of impact factors in the form of a transformation matrix function; and a set of output characteristics of the resulting perception of the same researched supported object (however in the individualistic perception of each individual subject of interaction with it). The possibility of encapsulation of artificial neural networks inside the aforementioned proposed form (of the supported software complexes’ impact factors representation) was investigated. And the use of multilayer perceptron was proposed and substantiated for the implementation of the appropriate encapsulations. An appropriate multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks, particularly a multilayer perceptron, has been developed and presented. Also, the applied practical problem of determining the deficient impact factors for each of the software complex’ support team members has been solved.
dc.format.extent197-207
dc.format.pages11
dc.identifier.citationПукач А. І. Розроблення моделі мультифакторного портрету суб’єктів підтримки програмних комплексів з застосуванням штучних нейронних мереж / А. І. Пукач, В. М. Теслюк // Комп'ютерні системи та мережі. — Львів : Видавництво Львівської політехніки, 2024. — Том 6. — № 2. — С. 197–207.
dc.identifier.citation2015Пукач А. І., Теслюк В. М. Розроблення моделі мультифакторного портрету суб’єктів підтримки програмних комплексів з застосуванням штучних нейронних мереж // Комп'ютерні системи та мережі, Львів. 2024. Том 6. № 2. С. 197–207.
dc.identifier.citationenAPAPukach, A., & Teslyuk, V. (2024). Rozroblennia modeli multyfaktornoho portretu subiektiv pidtrymky prohramnykh kompleksiv z zastosuvanniam shtuchnykh neironnykh merezh [Development a multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks]. Computer Systems and Networks, 6(2), 197-207. Lviv Politechnic Publishing House. [in Ukrainian].
dc.identifier.citationenCHICAGOPukach A., Teslyuk V. (2024) Rozroblennia modeli multyfaktornoho portretu subiektiv pidtrymky prohramnykh kompleksiv z zastosuvanniam shtuchnykh neironnykh merezh [Development a multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks]. Computer Systems and Networks (Lviv), vol. 6, no 2, pp. 197-207 [in Ukrainian].
dc.identifier.doiDOI: https://doi.org/10.23939/csn2024.02.197
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/123979
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofКомп'ютерні системи та мережі, 2 (6), 2024
dc.relation.ispartofComputer Systems and Networks, 2 (6), 2024
dc.relation.references1. Janssen A., Grützner L. & Breitner M. H. (2021). Why do Chatbots fail? A Critical Success Factors Analysis. In Proceedings of the 42nd International Conference on Information Systems, ICIS 2021, Austin, TX, USA, pp. 12–15. https://www.researchgate.net/profile/Antje-Janssen/publication/354811221_Why_do_Chatbots_fail_A_Critical_Success_ Factors_Analysis/links/614dbb5a154b3227a8a62ecc/Why-do-Chatbots-fail-A-Critical-Success-Factors-Analysis.pdf
dc.relation.references2. He J., Piorkowski D., Muller M. J., Brimijoin K., Houde S. & Weisz J. D. (2023b). Understanding how task dimensions impact automation preferences with a conversational task assistant. AutomationXP23: Intervening, Teaming, Delegating - Creating Engaging Automation Experiences, April 23rd, Hamburg, Germany, 6 pages. https://matthiasbaldauf.com/automationxp23/papers/AutomationXP23_paper11.pdf
dc.relation.references3. Corea C., Delfmann P. & Nagel S. (2020). Towards Intelligent Chatbots for Customer Care - Practice-Based Requirements for a Research Agenda. In: Proceedings of the 53rd Annual Hawaii International Conference on System Sciences HICSS 2020: Grand Wailea, Maui, Hawaii, January 7–10, p. 5819–5828. https://doi.org/10.24251/HICSS.2020.713
dc.relation.references4. Khankhoje R. (2023). An In-Depth Review of Test Automation Frameworks: Types and Trade-offs. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), Vol. 3, Issue 1, p.55–64. https://doi.org/10.48175/IJARSCT-13108
dc.relation.references5. Chunhua Deming C., Khair M. A., Mallipeddi S. R. & Varghese A. (2021). Software Testing in the Era of AI: Leveraging Machine Learning and Automation for Efficient Quality Assurance. Asian Journal of Applied Science and Engineering, Vol. 10, Issue 1, p. 66–76. https://doi.org/10.18034/ajase.v10i1.88
dc.relation.references6. Garousi V. & Mantyla M. V. (2016). When and what to automate in software testing? A multi-vocal literature review. Information and Software Technology, vol. 76, p. 92–117. https://doi.org/10.1016/j.infsof.2016.04.015
dc.relation.references7. Rafi S., Akbar M. A., AlSanad A. A., AlSuwaidan L., Abdulaziz AL-ALShaikh H. & AlSagri H. S. (2022). Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation. Mathematical Problems in Engineering, 2022, 2600160, 15 p. https://doi.org/10.1155/2022/2600160
dc.relation.references8. Azad N. (2023). The impact of DevOps critical success factors and organizational practices. 14th International Conference on Software Business, November 27–29, 2023, Lahti, Finland, 11 pages. https://ceur-ws.org/Vol-3621/phdpaper5. pdf
dc.relation.references9. Riungu-Kalliosaari L., Mäkinen S., Lwakatare L. E., Tiihonen J. & Männistö T..(2016). DevOps adoption benefits and challenges in practice: A case study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10027 LNCS, p. 590–597. https://doi.org/10.1007/978-3-319-49094-6_44.
dc.relation.references10. Van Belzen M., Trienekens J. & Kusters R. (2024). Validation and Clarification of Critical Success Factors of DevOps Processes. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024).–Vol. 2, p. 222–231. https://doi.org/10.5220/0012685800003690
dc.relation.references11. Van Belzen M., Trienekens J. & Kusters R. (2023). What Do Critical Success Factors of Collaboration Really Mean in the Context of DevOps? The Eighteenth International Conference on Software Engineering Advances (IARIA2023), p. 7–13. https://personales.upv.es/thinkmind/dl/conferences/icsea/icsea_2023/icsea_2023_1_20_10017.pdf
dc.relation.references12. Jim A., Shim H., Wang J., Wijaya L., Xu R., Khalajzadeh H., Grundy J. & Kanij T. (2021). Improving the Modelling of Human-centric Aspects of Software Systems: A Case Study of Modelling End User Age in Wirefame Designs. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering. ENASE;ISBN 978-989-758-508-1; ISSN 2184-4895, SciTePress, p. 68–79. https://doi.org/10.5220/0010403000680079
dc.relation.references13. Wang J., Xu Z., Wang X. & Lu J. (2022). A Comparative Research on Usability and User Experience of User Interface Design Software. International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), p.21–29. https://dx.doi.org/10.14569/IJACSA.2022.0130804
dc.relation.references14. Chhaya B., Jafer S. & Rice S. (2020). Human Factors Assessment of Scenario-driven Training in Web-based Simulation. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020), p. 189–196. https://doi.org/10.5220/0009820301890196
dc.relation.references15. Grundy J. (2021). Impact of End User Human Aspects on Software Engineering. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2021), p. 9–20. https://doi.org/10.5220/0010531800090020
dc.relation.references16. Chakraverty S. & Mall S. (2017). Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations (1st ed.). CRC Press. https://doi.org/10.1201/9781315155265
dc.relation.references17. Jain L. C. (2000). Recent Advances in Artificial Neural Networks (1st ed.). CRC Press. https://doi.org/10.1201/9781351076210
dc.relation.references18. Zhang B., Xu S., Lin M., Wang T. & Doermann D. (2023). Binary Neural Networks: Algorithms, Architectures, and Applications (1st ed.). CRC Press. https://doi.org/10.1201/9781003376132
dc.relation.references19. Zhang Y., Chen D. & Ye C. (2019). Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429426445
dc.relation.references20. Karacan C. Ö. (2021). Multilayer Perceptrons. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_455-1
dc.relation.references21. Reifman J. & Feldman E. E. (2002). Multilayer perceptron for nonlinear programming. Computers & Operations Research, Vol. 29, Issue 9, р. 1237–1250. https://doi.org/10.1016/S0305-0548(01)00027-2
dc.relation.references22. Shirvany Y., Hayati M. & Moradian R. (2009). Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations. Applied Soft Computing, Vol. 9, Issue 1, р. 20–29. https://doi.org/10.1016/j.asoc.2008.02.003
dc.relation.references23. Pukach A. I. & Teslyuk V. M. (2024). Model of decomposed insulating dominance for the analysis of influencing factors of software complexes support automation. Scientific Bulletin of UNFU, 34(5), 170–179.https://doi.org/10.36930/40340521
dc.relation.referencesen1. Janssen A., Grützner L. & Breitner M. H. (2021). Why do Chatbots fail? A Critical Success Factors Analysis. In Proceedings of the 42nd International Conference on Information Systems, ICIS 2021, Austin, TX, USA, pp. 12–15. https://www.researchgate.net/profile/Antje-Janssen/publication/354811221_Why_do_Chatbots_fail_A_Critical_Success_ Factors_Analysis/links/614dbb5a154b3227a8a62ecc/Why-do-Chatbots-fail-A-Critical-Success-Factors-Analysis.pdf
dc.relation.referencesen2. He J., Piorkowski D., Muller M. J., Brimijoin K., Houde S. & Weisz J. D. (2023b). Understanding how task dimensions impact automation preferences with a conversational task assistant. AutomationXP23: Intervening, Teaming, Delegating - Creating Engaging Automation Experiences, April 23rd, Hamburg, Germany, 6 pages. https://matthiasbaldauf.com/automationxp23/papers/AutomationXP23_paper11.pdf
dc.relation.referencesen3. Corea C., Delfmann P. & Nagel S. (2020). Towards Intelligent Chatbots for Customer Care - Practice-Based Requirements for a Research Agenda. In: Proceedings of the 53rd Annual Hawaii International Conference on System Sciences HICSS 2020: Grand Wailea, Maui, Hawaii, January 7–10, p. 5819–5828. https://doi.org/10.24251/HICSS.2020.713
dc.relation.referencesen4. Khankhoje R. (2023). An In-Depth Review of Test Automation Frameworks: Types and Trade-offs. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), Vol. 3, Issue 1, p.55–64. https://doi.org/10.48175/IJARSCT-13108
dc.relation.referencesen5. Chunhua Deming C., Khair M. A., Mallipeddi S. R. & Varghese A. (2021). Software Testing in the Era of AI: Leveraging Machine Learning and Automation for Efficient Quality Assurance. Asian Journal of Applied Science and Engineering, Vol. 10, Issue 1, p. 66–76. https://doi.org/10.18034/ajase.v10i1.88
dc.relation.referencesen6. Garousi V. & Mantyla M. V. (2016). When and what to automate in software testing? A multi-vocal literature review. Information and Software Technology, vol. 76, p. 92–117. https://doi.org/10.1016/j.infsof.2016.04.015
dc.relation.referencesen7. Rafi S., Akbar M. A., AlSanad A. A., AlSuwaidan L., Abdulaziz AL-ALShaikh H. & AlSagri H. S. (2022). Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation. Mathematical Problems in Engineering, 2022, 2600160, 15 p. https://doi.org/10.1155/2022/2600160
dc.relation.referencesen8. Azad N. (2023). The impact of DevOps critical success factors and organizational practices. 14th International Conference on Software Business, November 27–29, 2023, Lahti, Finland, 11 pages. https://ceur-ws.org/Vol-3621/phdpaper5. pdf
dc.relation.referencesen9. Riungu-Kalliosaari L., Mäkinen S., Lwakatare L. E., Tiihonen J. & Männistö T..(2016). DevOps adoption benefits and challenges in practice: A case study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10027 LNCS, p. 590–597. https://doi.org/10.1007/978-3-319-49094-6_44.
dc.relation.referencesen10. Van Belzen M., Trienekens J. & Kusters R. (2024). Validation and Clarification of Critical Success Factors of DevOps Processes. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024).–Vol. 2, p. 222–231. https://doi.org/10.5220/0012685800003690
dc.relation.referencesen11. Van Belzen M., Trienekens J. & Kusters R. (2023). What Do Critical Success Factors of Collaboration Really Mean in the Context of DevOps? The Eighteenth International Conference on Software Engineering Advances (IARIA2023), p. 7–13. https://personales.upv.es/thinkmind/dl/conferences/icsea/icsea_2023/icsea_2023_1_20_10017.pdf
dc.relation.referencesen12. Jim A., Shim H., Wang J., Wijaya L., Xu R., Khalajzadeh H., Grundy J. & Kanij T. (2021). Improving the Modelling of Human-centric Aspects of Software Systems: A Case Study of Modelling End User Age in Wirefame Designs. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering. ENASE;ISBN 978-989-758-508-1; ISSN 2184-4895, SciTePress, p. 68–79. https://doi.org/10.5220/0010403000680079
dc.relation.referencesen13. Wang J., Xu Z., Wang X. & Lu J. (2022). A Comparative Research on Usability and User Experience of User Interface Design Software. International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), p.21–29. https://dx.doi.org/10.14569/IJACSA.2022.0130804
dc.relation.referencesen14. Chhaya B., Jafer S. & Rice S. (2020). Human Factors Assessment of Scenario-driven Training in Web-based Simulation. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020), p. 189–196. https://doi.org/10.5220/0009820301890196
dc.relation.referencesen15. Grundy J. (2021). Impact of End User Human Aspects on Software Engineering. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2021), p. 9–20. https://doi.org/10.5220/0010531800090020
dc.relation.referencesen16. Chakraverty S. & Mall S. (2017). Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations (1st ed.). CRC Press. https://doi.org/10.1201/9781315155265
dc.relation.referencesen17. Jain L. C. (2000). Recent Advances in Artificial Neural Networks (1st ed.). CRC Press. https://doi.org/10.1201/9781351076210
dc.relation.referencesen18. Zhang B., Xu S., Lin M., Wang T. & Doermann D. (2023). Binary Neural Networks: Algorithms, Architectures, and Applications (1st ed.). CRC Press. https://doi.org/10.1201/9781003376132
dc.relation.referencesen19. Zhang Y., Chen D. & Ye C. (2019). Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429426445
dc.relation.referencesen20. Karacan C. Ö. (2021). Multilayer Perceptrons. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_455-1
dc.relation.referencesen21. Reifman J. & Feldman E. E. (2002). Multilayer perceptron for nonlinear programming. Computers & Operations Research, Vol. 29, Issue 9, r. 1237–1250. https://doi.org/10.1016/S0305-0548(01)00027-2
dc.relation.referencesen22. Shirvany Y., Hayati M. & Moradian R. (2009). Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations. Applied Soft Computing, Vol. 9, Issue 1, r. 20–29. https://doi.org/10.1016/j.asoc.2008.02.003
dc.relation.referencesen23. Pukach A. I. & Teslyuk V. M. (2024). Model of decomposed insulating dominance for the analysis of influencing factors of software complexes support automation. Scientific Bulletin of UNFU, 34(5), 170–179.https://doi.org/10.36930/40340521
dc.relation.urihttps://www.researchgate.net/profile/Antje-Janssen/publication/354811221_Why_do_Chatbots_fail_A_Critical_Success_
dc.relation.urihttps://matthiasbaldauf.com/automationxp23/papers/AutomationXP23_paper11.pdf
dc.relation.urihttps://doi.org/10.24251/HICSS.2020.713
dc.relation.urihttps://doi.org/10.48175/IJARSCT-13108
dc.relation.urihttps://doi.org/10.18034/ajase.v10i1.88
dc.relation.urihttps://doi.org/10.1016/j.infsof.2016.04.015
dc.relation.urihttps://doi.org/10.1155/2022/2600160
dc.relation.urihttps://ceur-ws.org/Vol-3621/phdpaper5
dc.relation.urihttps://doi.org/10.1007/978-3-319-49094-6_44
dc.relation.urihttps://doi.org/10.5220/0012685800003690
dc.relation.urihttps://personales.upv.es/thinkmind/dl/conferences/icsea/icsea_2023/icsea_2023_1_20_10017.pdf
dc.relation.urihttps://doi.org/10.5220/0010403000680079
dc.relation.urihttps://dx.doi.org/10.14569/IJACSA.2022.0130804
dc.relation.urihttps://doi.org/10.5220/0009820301890196
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dc.relation.urihttps://doi.org/10.1201/9781315155265
dc.relation.urihttps://doi.org/10.1201/9781351076210
dc.relation.urihttps://doi.org/10.1201/9781003376132
dc.relation.urihttps://doi.org/10.1201/9780429426445
dc.relation.urihttps://doi.org/10.1007/978-3-030-26050-7_455-1
dc.relation.urihttps://doi.org/10.1016/S0305-0548(01)00027-2
dc.relation.urihttps://doi.org/10.1016/j.asoc.2008.02.003
dc.relation.urihttps://doi.org/10.36930/40340521
dc.rights.holder© Національний університет „Львівська політехніка“, 2024
dc.rights.holder© Пукач А. І., Теслюк В. М., 2024
dc.subjectмодель
dc.subjectштучні нейронні мережі
dc.subjectбагатошаровий перцептрон
dc.subjectфак- тори впливу
dc.subjectмультифакторний портрет
dc.subjectпрограмні комплекси
dc.subjectпідтримка
dc.subjectавтома- тизація
dc.subjectmodel
dc.subjectartificial neural networks
dc.subjectmultilayer perceptron
dc.subjectimpact factors
dc.subjectmultifactor portrait
dc.subjectsoftware complexes
dc.subjectsupport
dc.subjectautomation
dc.subject.udc004.8
dc.titleРозроблення моделі мультифакторного портрету суб’єктів підтримки програмних комплексів з застосуванням штучних нейронних мереж
dc.title.alternativeDevelopment a multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks
dc.typeArticle

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Комп'ютерні системи та мережі. – 2024. – Том 6, № 2