Intelligent Network Architecture Development for E-Business Processes Based on Ontological Models

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Author(s)

Yevgen Burov 1 Victoria Vysotska 1 Lyubomyr Chyrun 2 Yuriy Ushenko 3,* Dmytro Uhryn 3 Zhengbing Hu 4

1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

2. Ivan Franko National University of Lviv, Lviv, 79000, Ukraine

3. Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine

4. School of Computer Science, Hubei University of Technology, Wuhan, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2024.05.01

Received: 24 May 2024 / Revised: 20 Jun. 2024 / Accepted: 15 Jul. 2024 / Published: 8 Oct. 2024

Index Terms

electronic business, business analytics, intelligent network, business analytics model, business process, networks architecture, intelligent system, system modelling, intelligent agent, intelligent system of business analytics

Abstract

The use of ontological models for intelligent systems construction allows for improved quality characteristics at all stages of the life cycle of a software product. The main source of improvement in quality characteristics is the possibility of reusing the conceptualization and code provided by the corresponding models. Due to the use of a single conceptualization when creating various software products, the degree of interoperability and code portability increases. The new-generation electronic business analytics systems implementation is based on the use of active models for business processes (BP). Such models, on the one hand, reflect the BPs taking place in the organization on a real-time scale, and on the other hand, embody corporate and other regulatory rules and restrictions and monitor their compliance. The purpose of this article is to research the methods of presenting and building active executable BP models, determining the methods of their execution and coordination, and building the resulting intelligent network of BP models. In the process of its implementation, such a network ensures the implementation, support of decision-making and compliance with regulatory rules in the relevant real BPs. A formal specification of an intelligent system for modelling a complex of BPs of the enterprise using models has been proposed. A hierarchical approach to the introduction of intelligent functions into the modelling system has been proposed. The simulation system is designed to be used for the design and management of complex intelligent systems. Achieving the set goal involves solving several development tasks: methods of presenting BP models for different types of such models; methods of analysis and display of time relations and attributes in BP models; ways of presenting the association of artefacts, and business analytics models with individual BP operations; metric ratios for evaluating the quality of process execution; methods of interaction of various BPs and coordination of their implementation. The purpose of functioning an intelligent model-driven software system is achieved through the interaction of a large number of simple models. At the same time, each model encapsulates a certain aspect of the expert's knowledge about the subject area. To apply executable conceptual models in the field of modelling BPes, it is necessary to determine the types of conceptual models used, their purpose and functions, and the role they play in the operation of an intelligent system. Models used in modelling BPes can be classified according to various characteristics. At the same time, the same model can be included in different classifications. 

Cite This Paper

Yevgen Burov, Victoria Vysotska, Lyubomyr Chyrun, Yuriy Ushenko, Dmytro Uhryn, Zhengbing Hu, "Intelligent Network Architecture Development for E-Business Processes Based on Ontological Models", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.16, No.5, pp. 1-54, 2024. DOI:10.5815/ijieeb.2024.05.01

Reference

[1]Oleh Veres, Pavlo Ilchuk, Olha Kots, The Concept of Using Artificial Intelligence Methods in Debt Financing of Business Entities, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 1542-1556
[2]Uhryn, D., Lytvyn, V., & Lendiuk, T. (2021, September). Method of Selecting and Determining the Free Parameters of Swarm Intelligent Algorithms for Optimizing Solutions in GIS. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 1, pp. 86-93). IEEE.
[3]Aamodt A. Case-based reasoning: Foundational issues, methodological variations, and system approaches / A. Aamodt, E. Plaza, AI Communications, Vol. 7, Issue 1, 1994, Р. 39–59.
[4]Balmelli L, Brown D, Cantor M, Mott M. Model-driven systems development // IBM Systems Journal, vol 45, Number 3, 2006. 
[5]MDA Distilled, Principles of Model Driven Architecture, Stephen Mellor, Kendall Scott, Axel Uhl, Dirk Weise, Addison-Wesley Professional, 2004. 
[6]Aleksandrov A. BI 2.0: prototype of the new architecture of business analytics. Open Systems No 5, 2007. 
[7]Babichev, S., Korobchynskyi, M., Rudenko, M., & Batenko, H. Applying biclustering technique and gene ontology analysis for gene expression data processing, CEUR Workshop Proceedings 3675 (2023). 14-28. https://ceur-ws.org/Vol-3675/paper2.pdf
[8]Chen, S. H., Babichev, Y., Rodrigues, N., Voskas, D., Ling, L., Nguyen, V. P., & Dumont, D. J. (2005). Gene expression analysis of Tek/Tie2 signaling. Physiological genomics, 22(2), 257-267.
[9]V. Lytvyn, V. Vysotska, P. Pukach, M. Vovk, D. Ugryn, Method of functioning of intelligent agents, designed to solve action planning problems based on ontological approach, volume 3/2(87) of Eastern-European Journal of Enterprise Technologies, 2017, pp. 11-17. DOI: 10.15587/1729-4061.2017.103630
[10]Lytvyn, V.V.: An approach to intelligent agent construction for determining the group of bank risk basing on ontology. In: Actual Problems of Economics (7), 314-320. (2011)
[11]Shakhovska, N.B.: Methods of customer data processing using intelligent agent of data sources structure determination. In: Actual Problems of Economics, (7), 338-346. (2011)
[12]V. Lytvyn, R. Vovnyanka, O. Oborska, D. Dosyn, V. Vysotska, V. Panasyuk, Intelligent Agent Behavior Simulation Based on Reinforcement Learning, in: IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT, 2020, 1, pp. 285-290. DOI: 10.1109/CSIT49958.2020.9321959
[13]V. Lytvyn, M. Bublyk, V. Vysotska, V. Panasyuk, O. Brodyak, M. Luchkevych, Modelling of the Intelligent Agent’s Behavior Scheduler Based on Petri Nets and Ontological Approach, in: IEEE International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan, 2021. https://ieeexplore.ieee.org/document/9465994
[14]Anand, V., Hota, C. (2020). MOTIVATION OF PARTICIPANTS IN CROWDSOURCING PLATFORMS USING INTELLIGENT AGENTS. International Journal of Computing, 19(1), 78-87.
[15]P. Kravets, V. Lytvyn, V. Vysotska, Y. Burov, Promoting training of multi-agent systems, volume Vol-2608 of CEUR Workshop Proceedings, 2020, pp. 364-378.
[16]K. Melnyk, Y. Kravets, I. Liutenko, S.Yershova, O. Ivashchenko, D. Yershov, O. Odyntsova, Multi-Agent Approach for the Unification of Meteorological Data, CEUR Workshop Proceedings, Vol-3403, 2023, 476-486.
[17]M. Korablyov, N. Axak, O. Fomichov, V.Hnidenko, Multi-agent Clinical Decision Support System using Case-Based Reasoning. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 1466-1476.
[18]Kravets, P.: Game method for coalitions formation in multi-agent systems. In: International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT, 1-4. (2018)
[19]Kravets, P., Pasichnyk, V., Kunanets, N., Veretennikova, N., Husak, O., Adaptive Strategies in the Multi-agent “Predator-Prey” Models. In: Advances in Intelligent Systems and Computing, 2021, 1247 AISC, pp. 285–295
[20]Kravets, P., Pasichnyk, V., Kunanets, N., Veretennikova, N., Game Method of Event Synchronization in Multi-agent Systems. In: Advances in Intelligent Systems and Computing, 2020, 938, pp. 378–387
[21]P. Kravets, V. Lytvyn, I. Dobrotvor, O. Sachenko, V. Vysotska, A. Sachenko, Matrix Stochastic Game with Q-learning for Multi-agent Systems, volume 83 of Lecture Notes on Data Engineering and Communications Technologies, 2021, pp. 304-314, https://link.springer.com/chapter/10.1007/978-3-030-80472-5_26
[22]Kravets, P., Game method for coalitions formation in multi-agent systems. In: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2018 - Proceedings, 2018, 1, pp. 1–4, 8526610
[23]Cherednichenko, O., Matveiev, O., Yanholenko, O., Maneva, R., Multi-agent modeling of project management processes in distributed teams. In: CEUR Workshop Proceedings, 2021, 2851, pp. 132–142
[24]P. Kravets, V. Lytvyn, V. Vysotska, Y. Ryshkovets, S. Vyshemyrska, S. Smailova, Dynamic Coordination of Strategies for Multi-agent Systems, volume 1246 of Advances in Intelligent Systems and Computing, 2020, pp. 653-670. DOI: 10.1007/978-3-030-54215-3_42
[25]J. Rogushina, Ontological Approach in the Smart Data Paradigm as a Basis for Open Data Semantic Markup, CEUR Workshop Proceedings, Vol-3403, 2023, 12-27.
[26]V. Hryhorovych, Calculation of the Semantic Distance between Ontology Concepts: Taking into Account Critical Nodes, CEUR Workshop Proceedings, Vol-3396, 2023, 206-216.
[27]Viktor Hryhorovych, Analysis of Scientific Texts by Semantic Inverse-Additive Metrics for Ontology Concepts, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 801-816
[28]V. Hryhorovych, Construction of Semantic Metric for Measuring the Distance between Ontology Concepts. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 498-510.
[29]Sergii Babichev, Igor Liakh, Jíří Škvor et al. Integrating Data Mining, Deep Learning, and GeneOntology Analysis for Gene Expression-BasedDisease Diagnosis Systems, 14 March 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3978499/v1]
[30]Babichev, S., Liakh, I., & Kalinina, I. (2024). Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data. IEEE Access.
[31]S. Albota, Creating a Model of War and Pandemic Apprehension: Textual Semantic Analysis, CEUR Workshop Proceedings, Vol-3396, 2023, 228-243.
[32]Olizarenko S., Radchenko V. (2021). Method for determining the semantic similarity of arbitrary length texts using the transformers models. Advanced Information Systems, 5(2), 126–130. https://doi.org/10.20998/2522-9052.2021.2.18
[33]Basyuk T., Vasilyuk A., Lytvyn V. Mathematical model of semantic search and search optimization // CEUR Workshop Proceedings. – 2019. – Vol. 2362 : Proceedings of the 3rd International conference on computational linguistics and intelligent systems, COLINS-2019, Kharkiv, Ukraine, 18-19 April 2019. – P. 96–105.
[34]Sergey Orekhov, Advanced Method of Synthesis of Semantic Kernel of E-content, CEUR Workshop Proceedings, Vol-3312, 2022, pp. 87-97
[35]Natalia Sharonova, Iryna Kyrychenko, Iryna Gruzdo, Glib Tereshchenko, Generalized Semantic Analysis Algorithm of Natural Language Texts for Various Functional Style Types, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 16-26
[36]Sergey Orekhov, Henadii Malyhon, Method for Synthesizing the Semantic Kernel of Web Content, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 127-137
[37]Solomiia Albota, Modelling the Impact of the Pandemic on Online Communication: Textual Semantic Analysis, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 471-486
[38]Viktor Shynkarenko, Larysa Zhuchyi, Semantic Checking of Different Type Information Sources About Permitted Speeds in Railway Transport, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 711-723
[39]Sergey Orekhov, Henadii Malyhon, Tetiana Goncharenko, Mathematical Model of Semantic Kernel of WEB site, in: CEUR Workshop Proceedings, Vol-2917, 2021, pp. 273-282.
[40]L. Savytska, N. Vnukova, I. Bezugla, V. Pyvovarov, M. Turgut Sübay, Using Word2vec Technique to Determine Semantic and Morphologic Similarity in Embedded Words of the Ukrainian Language. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 235-248.
[41]S. Albota, Linguistically Manipulative, Disputable, Semantic Nature of the Community Reddit Feed Post. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 769-783.
[42]Melnykova, N., Markiv, O.: Semantic approach to personalization of medical data. In: Computer Sciences and Information Technologies, CSIT, 59-61. (2016)
[43]Fiser, J., Lytvynenko, V., & Mashkov, V. (2015). Representation of System Level Self-Diagnosis in Python Programming Language. Electrotechnic and Computer Systems, (17 (93)), 48-54.
[44]Khomovyi, S., Tomilova-Yaremchuk, N., Khomovyi, M., Lytvynenko, V., Khomiak, N., & Liudvenko, D. (2023). Psychological aspects of the formation of the accounting department at agricultural enterprises and elements of its audit.
[45]V. Lytvyn, V. Vysotska, Y. Burov, O. Brodyak, Approach to Automatic Construction of Interpretation Functions during Ontology Learning, in: IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT, 2020, 1, pp. 267-271. DOI: 10.1109/CSIT49958.2020.9321920
[46]T. Basyuk, A. Vasyliuk, Approach to a Subject Area Ontology Visualization System Creating. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 528-540. T. Basyuk, A. Vasyliuk, Approach to a Subject Area Ontology Visualization System Creating. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 528-540.
[47]Dmytro Dosyn, Yousef Ibrahim Daradkeh, Vira Kovalevych, Mykhailo Luchkevych, Yaroslav Kis, Domain Ontology Learning using Link Grammar Parser and WordNet, CEUR Workshop Proceedings, Vol-3312, 2022, pp. 14-36
[48]V. Lytvyn, D. Dosyn, V. Vysotska, A. Demchuk, L. Demkiv, I. Lytvyn, Intellectual agent construction method based on the subject field ontology, in: IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT, 2020, 1, pp. 40-46. DOI: 10.1109/CSIT49958.2020.9322032
[49]Taras Batiuk, Lyubomyr Chyrun, Oksana Oborska, Ontology Model and Ontological Graph for Development of Decision Support System of Personal Socialization by Common Relevant Interests, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 877-903
[50]N. Khairova, A. Kolesnyk, O. Mamyrbayev, G. Ybytayeva, Y. Lytvynenko, Automatic Multilingual Ontology Generation Based on Texts Focused on Criminal Topic. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 108-117.
[51]A. Bakurova, M. Pasichnyk, E. Tereschenko, Development of a Productive Credit Decision-Making System Based on the Ontology Model. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 580-589.
[52]Kunanets, N., Matsiuk, H.: Use of the Smart City Ontology for Relevant Information Retrieval. In: CEUR Workshop Proceedings, Vol-2362, 322-333. (2019)
[53]Chen, J., Dosyn, D., Lytvyn, V., Sachenko, A.: Smart data integration by goal driven ontology learning. In: Advances in Intelligent Systems and Computing, 529, 283-292. (2017)
[54]Garanina, N., Sidorova, E., Kononenko, I., Gorlatch, S. (2017). USING MULTIPLE SEMANTIC MEASURES FOR COREFERENCE RESOLUTION IN ONTOLOGY POPULATION. International Journal of Computing, 16(3), 166-176.
[55]Burov, Y., Mykich, K., Karpov, I., Building a Versatile Knowledge-Based System Based on Reasoning Services and Ontology Representation Transformations 2020 IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020 - Proceedings, 2020, 2, pp. 255–260, 9321943
[56]Mathias Weske. Business Process Management. Concepts, Languages, Architectures. Springer-Verlag Berlin Heidelberg, 2007.
[57]Unified Modeling Language http://en.wikipedia.org/wiki/Unified_Modeling_ Language. 
[58]Hans-Erik Eriksson and Magnus Penker. Business Modeling with UML: Business Patterns at Work. John Wiley & Sons,  2000. 
[59]Martin Owen and Jog Raj. BPMN and Business Process Management. An Introduction to the New Business Process Modeling Standard, Popkin Software whitepaper, 2003. 
[60]OMG Systems Modeling Language (OMG SysML™), V1.0. OMG Available Specification. OMG Document Number: formal/2007-09-01. Standard document URL: http://www.omg.org/spec/SysML/1.0/PDF
[61]Business Process Execution Language http://en.wikipedia.org/wiki/ Business_Process_ Execution_ Language. 
[62]BPEL Cookbook Best Practices for SOA-based integration and composite applications development. Editors Harish Gaur, Markus Zirn. Packt Publishing Birmingham – Mumbai, 2006. 
[63]Norman D., Ortoni E., Russell D. Emotional reaction and construction of computers. Open Systems, 2003.
[64]Burov E.V. System of Formal Specifications for Designing Distributed Information Systems. Bulletin of Lviv Polytechnic State University "Information Systems and Networks". – 2000. – № 406. 
[65]Lytvyn, V.: The similarity metric of scientific papers summaries on the basis of adaptive ontologies. In: Proceedings of 7th International Conference on Perspective Technologies and Methods in MEMS Design, MEMSTECH, 162. (2011)
[66]V. Lytvyn, V. Vysotska, D. Dosyn, O. Lozynska, O. Oborska, Methods of Building Intelligent Decision Support Systems Based on Adaptive Ontology, in: Proceedings of the 2nd International Conference on Data Stream Mining and Processing, DSMP, 2018, pp. 145-150. DOI: 10.1109/DSMP.2018.8478500
[67]Pasyeka, M., Pasieka, N., Sheketa, V., Romanyshyn, Y., Kondur, O., Varvaruk, M.: The use of Dyadic and Advisory Opinions in the Model of Group Dynamics of the Decision-Making Process for Software System Developers. In: CEUR Workshop Proceedings, Vol-2631, 304-313. (2020)
[68]V. Dyomina, T. Bilova, I. Pobizhenko, O. Chala, T. Domina, Representation of Knowledge by Temporal Cases in Humanitarian Response, CEUR Workshop Proceedings, Vol-3403, 2023, 126-136.
[69]Y. Burov, I. Karpov, Contextual Concept Meaning Alignment Based on Prototype Theory, CEUR Workshop Proceedings, Vol-3403, 2023, 137-146.
[70]O. Kudryk, O. Bisikalo, Y. Ivanov, Knowledge Base of Intelligent Information System for Prediction of Phase Stability of Solid Solutions, CEUR Workshop Proceedings, Vol-3403, 2023, 147-156.
[71]O. Karataiev, D. Sitnikov, N. Sharonova, A Method for Investigating Links between Discrete Data Features in Knowledge Bases in the Form of Predicate Equations, CEUR Workshop Proceedings, Vol-3387, 2023, pp. 224-235.
[72]M. Makaruk, A. Nazarov, I. Shubin, N. Shanidze, Knowledge Representation Method for Object Recognition in Nonlinear Radar Systems. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 948-958.
[73]I. Shubin, A. Kozyriev, V. Liashik, G. Chetverykov, Methods of Adaptive Knowledge Testing Based on the Theory of Logical Networks. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 1184-1193.
[74]Volodymyr Hnatushenko, Victoriia Hnatushenko, Nataliia Dorosh, Nataliia Solodka, Oksana Liashenko. Non-relational approach to developing knowledge bases of expert system prototype. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2022, № 2. P.112-117. https://doi.org/10.33271/nvngu/2022-2/112
[75]Roman Sytnyk, Viktoriia Hnatushenko, Volodymyr Hnatushenko. Decentralized Information System for Supply Chain Management Using Blockchain. IntelITSIS’2022: 3nd International Workshop on Intelligent Information Technologies and Systems of Information Security, March 23–25 2022, Khmelnytskyi, Ukraine. http://ceur-ws.org/Vol-3156/paper45.pdf 
[76]Viktor Khavalko, Iryna Bilyk, Oleksandra Hasko, Iryna Protsyk, Architecture of Online Automated Knowledge Testing Systems, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 1583-1593
[77]Roman Miroshnyk, Igor Matviy, Yuliya Voytsekhovska, Yaroslav Kis, Iryna Bahlai, Current Trends in Knowledge Management: Problems and Challenges, CEUR Workshop Proceedings, Vol-3171, 2022, pp. 1708-1718
[78]Vasyl Lenko, Volodymyr Pasichnyk, Natalia Kunanets, Yurii Shcherbyna, Knowledge Representation and Automated Formal Reasoning in Description Logic ALC, in: CEUR Workshop Proceedings, Vol-2917, 2021, pp. 26-39.
[79]Y. Burov, Knowledge Based Situation Awareness Process Based on Ontologies. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 413-423.
[80]A. Yarovyi, D. Kudriavtsev, Method of Multi-Purpose Text Analysis Based on a Combination of Knowledge Bases for Intelligent Chatbot. In: CEUR Workshop Proceedings, Vol-2870, 2021, pp. 1238-1248.
[81]Bomba, A., Nazaruk, M., Kunanets, N., Pasichnyk, V.: Modeling the Dynamics of Knowledge Potential of Agents in the Educational Social and Communication Environment. In: Advances in Intelligent Systems and Computing IV, Springer Nature Switzerland AG, Springer, Cham, 1080, 17-24. (2020)
[82]Bublyk, M., Rybytska, O., Karpiak, A., Matseliukh, Y.: Structuring the fuzzy knowledge base of the IT industry impact factors. In: Computer sciences and information technologies (CSIT). (2018).
[83]Sachenko, S., Rippa, S., Krupka, Y.: Pre-Conditions of Ontological Approaches Application for Knowledge Management in Accounting. In: International Workshop on Аntelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 605-608. (2009)
[84]Dosyn, D., Lytvyn, V., Kovalevych, V., Oborska, O., Holoshchuk, R.: Knowledge discovery as planning development in knowledgebase framework. In: Modern Problems of Radio Engineering, Telecommunications and Computer Science, Proceedings of the 13th International Conference on TCSET, 449-451. (2016)
[85]Bomba, A., Nazaruk, M., Kunanets, N., Pasichnyk, V. (2017). CONSTRUCTING THE DIFFUSION-LIKE MODEL OF BICOMPONENT KNOWLEDGE POTENTIAL DISTRIBUTION. International Journal of Computing, 16(2), 74-81. 
[86]Brand E. Decision Trees / E. Brand, R. Gerritsen – DBMS, № 7, 1998, P. 32–45.
[87]V. Lytvyn, V. Vysotska, D. Dosyn, Y. Burov, Method for ontology content and structure optimization, provided by a weighted conceptual graph, volume 15(2) of Webology, 2018, pp. 66-85.
[88]ISO/IEC 9126-1:2001, URL: http://www.iso.org/iso/catalogue_detail.htm?csnumber=22749.
[89]ISO 8402:1994, URL: http://www.iso.org/iso/catalogue_detail.htm?csnumber=20115.
[90]ISO/IEC 25010:2011. Systems and software engineering -– (SQuaRE), URL: http://www.iso.org/iso/home/store/catalogue_ics/catalogue_detail_ics.htm?csnumber=35733.
[91]GOST 28195-89, URL: http://www.gametest.ru/doc/sw/28195_89.pdf.
[92]Charvat J., Project Management Methodologies/ Charvat, J.– Wiley, 2003.
[93]Nuñez-Varela, A. S., Pérez-Gonzalez, H. G., Martínez-Perez, F. E., & Soubervielle-Montalvo, C. (2017). Source code metrics: A systematic mapping study. Journal of Systems and Software, 128, 164-197.