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  3. Volume 6, Number 2, 2024
  4. Development a Multifactorial Portrait Model of Software Complexes’ Supporting Subjects, Using Artificial Neural Networks

Development a Multifactorial Portrait Model of Software Complexes’ Supporting Subjects, Using Artificial Neural Networks

CSN.
2024;
Volume 6, Number 2
: pp. 197-207
https://doi.org/10.23939/csn2024.02.197
Authors:
  1. Andrii Pukach
  2. Vasyl Teslyuk
1
Lviv Polytechnic National University, Department of Automated Control Systems
2
Lviv Polytechnic National University, Lviv, Ukraine

Impact 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.

model
artificial neural networks
multilayer perceptron
impact factors
multifactor portrait
software complexes
support
automation
  1. 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...
  2. 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...
  3. 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, pp. 5819-5828. https://doi.org/10.24251/HICSS.2020.713
  4. 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), Volume 3, Issue 1, pages 55-64. https://doi.org/10.48175/IJARSCT-13108
  5. 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, pages 66-76. https://doi.org/10.18034/ajase.v10i1.88
  6. 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, pp. 92–117. https://doi.org/10.1016/j.infsof.2016.04.015
  7. 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 pages. https://doi.org/10.1155/2022/2600160
  8. 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/phd-paper5.pdf
  9. L. Riungu-Kalliosaari, S. Mäkinen, L. E. Lwakatare, J. Tiihonen, & T. Männistö. (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, pp. 590–597. https://doi.org/10.1007/978-3-319-49094-6_44.
  10. 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) - Volume 2, pages 222-231. https://doi.org/10.5220/0012685800003690
  11. 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 (IARIA 2023), pp. 7-13. https://personales.upv.es/thinkmind/dl/conferences/icsea/icsea_2023/icse...
  12. 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, pages 68-79. https://doi.org/10.5220/0010403000680079
  13. 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), pp.21-29. https://dx.doi.org/10.14569/IJACSA.2022.0130804
  14. 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), pages 189-196. https://doi.org/10.5220/0009820301890196
  15. 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), pages 9-20. https://doi.org/10.5220/0010531800090020
  16.  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
  17. Jain, L.C. (2000). Recent Advances in Artificial Neural Networks (1st ed.). CRC Press. https://doi.org/10.1201/9781351076210
  18. 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
  19. 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
  20. 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
  21. Reifman, J. & Feldman, E.E. (2002). Multilayer perceptron for nonlinear programming. Computers & Operations Research, Volume 29, Issue 9, Pages 1237-1250. https://doi.org/10.1016/S0305-0548(01)00027-2
  22. 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, Volume 9, Issue 1, Pages 20-29. https://doi.org/10.1016/j.asoc.2008.02.003
  23. 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