The Main Approaches to the Formation of Mathematical and Simulation Models Based on Knowledge Bases in Software Development
( Pp. 142-151)
More about authors
Astafev Rustam U.
senior lecturer, Department of Industrial Programming; Institute of Advanced Technologies and Industrial Programming
MIREA – Russian Technological University
Moscow, Russian Federation
MIREA – Russian Technological University
Moscow, Russian Federation
Abstract:
This article discusses the application of mathematical and simulation models based on knowledge bases in the software development process. The purpose of the study is to analyze the impact of these models on the quality and efficiency of creating software systems, as well as to identify the key stages of their integration into the development process. In the course of the study, an analysis of existing practices was carried out, which allowed us to draw several conclusions. Firstly, the use of mathematical and simulation models significantly improves the understanding of complex interactions in software systems and contributes to a more accurate prediction of their behavior. Secondly, access to knowledge bases speeds up the modeling process and increases its accuracy, which leads to more informed decision-making and reduced risks. Finally, integrating these approaches into software development allows teams to remain competitive and adaptive in a rapidly changing technology environment. Thus, the article emphasizes the importance and necessity of using mathematical and simulation models to improve the quality of software development.
How to Cite:
Astafev R.U. The Main Approaches to the Formation of Mathematical and Simulation Models Based on Knowledge Bases in Software Development. Computational Nanotechnology. 2024. Vol. 11. No. 5. Pp. 142–151. (In Rus.). DOI: 10.33693/2313-223X-2024-11-5-142-151. EDN: CCLNZK
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Piskunov I.V., Pankin A.A., Bashkirtseva N.Yu. Mathematical modeling of processes for the production of petroleum oxidized bitumen (review). Bulletin of the Technological University. 2022. Vol. 25. No. 4. Pp. 83–94. (In Rus.). DOI: 10.55421/1998-7072_2022_25_4_83. EDN: UTDUZA.
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Feldmann F., Nødland O., Sagen J. et al. IORSim: A mathematical workflow for field-scale geochemistry simulations in porous media. Transp. Porous Med. 2024. No. 151. Pp. 1781–1809. DOI: 10.1007/s11242-024-02094-9.
Waleed M., Qazi A.Kh., Waqas R. et al. Development of BIM-based tunnel information modeling prototype for tunnel design. Advances in Civil Engineering. 2024. No. 8118578. 18 p. DOI: 10.1155/2024/8118578.
Pal M.K., Ram K. Application of mathematical modelling techniques in optimal design of wastewater treatment plants. In: Biological and hybrid wastewater treatment technology. Earth and environmental sciences library. M.M. Ghangrekar, S. Yadav, R.N. Yadava (eds.). Springer, Cham., 2024. DOI: 10.1007/978-3-031-63046-0_18.
Perazzolo S. SAAM II: A general mathematical modeling rapid prototyping environment. CPT Pharmacometrics Syst Pharmacol. 2024. No. 13. Pp. 1088–1102. DOI: 10.1002/psp4.13181.
Shchekaturov A.M., Timofeev K.A., Kozlov O.S. Method to develop functional software for NPP APCS using model-oriented approach in SimInTech. University Scientific Journal. 2015. No. 15. Pp. 80–87. (In Rus.). EDN: VCMNXN.
Astafiev R.U., Shamin R.V. Fundamentals of statistical tools for analyzing and forecasting the quality of software products. Science and Business: Ways of Development. 2024. No. 5 (155). Pp. 86–90. (In Rus.). EDN: DOLXUJ.
Astafiev R.U., Derevyanko N.V. Implementation of an algorithm for selecting server equipment for modernizing the information system of a production workshop. Modern Science: Actual Problems of Theory and Practice. Series: Natural and Technical Sciences. 2024. No. 5. Pp. 27–32. (In Rus.). DOI: 10.37882/2223-2966.2024.05.01. EDN: AMAYIV.
Astafiev R.U., Pronina E.V., Pikhtilkova O.A. et. al. Synthesis of an algorithm for selecting the implementation of methods for factor analysis of econometric data in R and Python. Moscow Economic Journal. 2023. Vol. 8. No. 4. (In Rus.). DOI: 10.55186/2413046X_2023_8_4_183. EDN: PQNFAF.
Bogomyagkov A.V., Pugin A.V. Improvement of the mathematical model of heat and mass transfer in a frozen rock mass, implemented in the frozenwall program. News of Tomsk Polytechnic University. Georesources Engineering. 2023. Vol. 334. No. 2. Pp. 164–174. (In Rus.). DOI: 10.18799/24131830/2023/2/3808. EDN: ABDTNB.
Zubarev V.R. Development of instrumental systems for analyzing the sustainability of an enterprise. Economics: Yesterday, Today, Tomorrow. 2024. Vol. 14. No. 2-1. Pp. 455–463. (In Rus.). DOI: 10.34670/AR.2024.36.42.028. EDN: ANLSZA.
Ivanov I.E., Murzin A.Yu. Determination of the statistical properties of a random error accompanying synchronized vector measurements of currents and voltages in steady state. Bulletin of the Ivanovo State Energy University. 2014. No. 3. Pp. 29–38. (In Rus.). EDN: SGIVGZ.
Kislitsyn E.V., Gorodnichev V.V. Simulation modeling of the development of individual branches of heavy industry. Business Informatics. 2021. Vol. 15. No. 1. Pp. 59–77. (In Rus.). DOI: 10.17323/2587-814X.2021.1.59.77. EDN: RADPCO.
Piskunov I.V., Pankin A.A., Bashkirtseva N.Yu. Mathematical modeling of processes for the production of petroleum oxidized bitumen (review). Bulletin of the Technological University. 2022. Vol. 25. No. 4. Pp. 83–94. (In Rus.). DOI: 10.55421/1998-7072_2022_25_4_83. EDN: UTDUZA.
Sidoryakina V.V. Mathematical model of the process of spreading oil pollution in coastal marine systems. Computational Mathematics and Information Technologies. 2023. Vol. 7. No. 4. Pp. 39–46. (In Rus.). DOI: 10.23947/2587-8999-2023-7-4-39-46. EDN: EKBWZN.
Shikov P.A. Application of tools for mathematical modeling and forecasting of enterprise sales volumes taking into account seasonal fluctuations. Modern Aspects of Economics. 2020. No. 2 (270). Pp. 79–86. (In Rus.). EDN: MOHQSD.
Keywords:
simulation modeling, knowledge bases, software development process, software quality, software product lifecycle, mathematical modeling methods.
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