THE METHOD OF ASSESSING THE RELIABILITY OF SOFTWARE SYSTEMS BASED ON A GRAPHIC MODEL OF THE DEPENDENCE OF METHODS OF THE SYSTEM UNDER TEST

Main Article Content

Svitlana Krepych
Iryna Spivak
Serhii Spivak
Roman Krepych

Abstract

Today, software has become an integral part of many areas of our daily life — from automation and optimization of production processes to the creation of individual comfort. Programs make our lives easier, solving tasks in seconds that used to take hours or even weeks, as well as giving us convenience and comfort that people of previous generations could not even dream of. In order to meet the growing demand for new IT software, the market around the world and in the country in particular is also growing and changing rapidly. According to the IT Ukraine Association, compared to 2017, the number of employed specialists in the labor market of Ukraine increased by approximately two times, and the volume of export of IT services - by two and a half. Despite the fact that due to the full-scale invasion, the pace of development has slowed down in 2022-2024, it is clear that the industry has not reached its peak, which means that it will continue to develop. In addition to the obvious changes related to the expansion of the market, there are also internal changes in the processes of the industry due to the desire to increase the speed of program development, as well as to reduce the final price of the software product. It is common knowledge that high quality software is an integral part of a successful product. However, even at a fairly low pace of development, developers often make mistakes that lead to serious problems, affecting security, reliability, and user satisfaction. So what can be said about the development in a short time? That is why ensuring the high quality of the software product is one of the main tasks that must be solved at the development stage. The object of the research is the process of assessing the reliability of software systems. The subject of the research is a method of assessing the reliability of software systems based on a graphic model of the dependence of the methods of the system under test. Conclusion: on the basis of the method of evaluating the reliability of software systems based on the graphical model of the dependence of the methods of the system under test, software was developed in the Java and Kotlin programming languages for evaluating the reliability index of software systems of any architectural complexity

Article Details

How to Cite
Krepych , S. ., Spivak , I. ., Spivak , S. ., & Krepych, R. . (2025). THE METHOD OF ASSESSING THE RELIABILITY OF SOFTWARE SYSTEMS BASED ON A GRAPHIC MODEL OF THE DEPENDENCE OF METHODS OF THE SYSTEM UNDER TEST. Advanced Information Systems, 9(2), 58–67. https://doi.org/10.20998/2522-9052.2025.2.08
Section
Information systems research
Author Biographies

Svitlana Krepych , Western Ukrainian National University, Ternopil

Candidate of Technical Sciences, Associate Professor, Associate Professor of Computer Science Department

Iryna Spivak , Western Ukrainian National University, Ternopil

Candidate of Technical Sciences, Associate Professor, Associate Professor of Computer Science Department

Serhii Spivak , Ternopil Ivan Puluj National Technical University, Ternopil

Doctor of Economic Sciences, Professor, Head of the Accounting and Audit Department

Roman Krepych, Kamianiets-Podilskyi State Institute, Kamianets-Podilskyi

lector

References

Yakovyna, V. and Symets, I. (2021), “Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains”, Procedia Computer Science, vol. 192, pp. 447–456, doi: https://doi.org/10.1016/j.procs.2021.08.046

Skanda, V.C., Srinivasa Prasad, S, Dheemanth, G.R. and Kumar, N.S. (2019), “Assessment of quality of program based on static analysis”, IEEE 10th International Conference on Technology for Education(T4E), pp. 276–277, doi: https://doi.org/10.1109/T4E.2019.00072

Lu, S., Li, H. and Jiang, Z. (2020), “Comparative study of open source software reliability assessment tools”, IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), China, pp. 49–55, doi: https://doi.org/10.1109/ICAIIS49377.2020.9194946

Yakovyna, V., Seniv, M., Symets, I. and Sambir, N. (2020), “Algorithms and software suite for reliability assessment of complex technical systems”, Radio Electronics, Computer Science, Control 4, pp. 163–177, doi: https://doi.org/10.15588/1607-3274-2020-4-16

San, K.K., Washizaki, H., Fukazawa, Y., Honda, K., Taga, M. and Matsuzaki, A. (2021), “Deep cross-project software reliability growth model using project similarity-based clustering”, Mathematics, vol. 9, no. 22, article number 2945, doi: https://doi.org/10.3390/math9222945

Krepych, S., Stakhiv, P. and Spivak, I., (2013), “Analysis of the tolerance area parameters REC based on technological area scattering”, 12th International Conference “The Experience of Designing and Application of CAD Systems in Microelectronics”, Polyana Svalyava, Ukraine, pp.179–180. available at: https://ieeexplore.ieee.org/document/6543231

Wu, C.-Y. and Huang, C.-Y. (2021), “A study of incorporation of deep learning into software reliability modeling and assessment”, IEEE Transactions on Reliability, vol. 70, no. 4, pp. 1621–1640, doi: https://doi.org/10.1109/TR.2021.3105531

Jagtap, M., Katragadda, P. and Satelkar, P. (2022), “Software reliability: development of software defect prediction models using advanced techniques”, Annual Reliability and Maintainability Symposium (RAMS), pp. 1–7, doi: https://doi.org/10.1109/RAMS51457.2022.9893986

Nafreen, M., Luperon, M., Fiondella, L., Nagaraju, V., Shi, Y. and Wandji, T. (2020), “Connecting software reliability growth Models to software defect tracking”, IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), pp.138–147, doi: https://doi.org/10.1109/ISSRE5003.2020.00022

Yakovyna, V. and Symets, I. (2021), “A method of high-order Markov chain representation through an equivalent first-order chain for software reliability assessment”, Computer systems and information technologies, vol. 3, pp. 66–73, doi: https://doi.org/10.31891/CSIT-2021-5-9

Jain, R. and Sharma, A. (2019), “Assessing software reliability using genetic algorithms”, The Journal of Engineering Research, [TJER], vol. 16(1), pp. 11–17, doi: https://doi.org/10.24200/tjer.vol16iss1

Micro, R., Chren, S. and Rossi, B. (2022), “Applicability of soft-ware reliability growth models to open source software”, 48th Euromicro Conference on Software Engineering and Acvanced Applications, (SEAA), pp. 255–262, doi: https://doi.org/10.1109/SEAA56994.2022.00047

Lu, S., Li, H. and Jiang, Z. (2020), “Comparative Study of Open Source Software Reliability Assessment Tools”, 2020 IEEE International Conference on Artificial Intelligence and Information Systems, (ICAIIS), Dalian, China, pp. 49–55, doi: https://doi.org/10.1109/ICAIIS49377.2020.9194946

Kim, T., Ryu, D. and Baik, J. (2024), “Enhancing software reliability growth modeling: a comprehensive analysis of historical datasets and optimal model selections”, IEEE 24th International Conference on Software Quality, Reliability and Security, QRS, pp. 147–158, doi: https://doi.org/10.1109/QRS62785.2024.00024

Chen, Y., Yan, X. and Khan, A.A. (2019), “A Novel Reliability Assessment Method Based on the Effects of Components”, IEEE 19th International Conference on Software Quality, Reliability and Security (QRS), Sofia, Bulgaria, pp. 69–76, doi: https://doi.org/10.1109/QRS.2019.00022

Nafreen, M., Luperon, M., Fiondella, L., Nagaraju, V., Shi, Y. and Wandji, T. (2020), “Connecting Software Reliability Growth Models to Software Defect Tracking”, IEEE 31st International Symposium on Software Reliability Engineering, ISSRE, Coimbra, Portugal, pp. 138–147, doi: https://doi.org/10.1109/ISSRE5003.2020.00022

Saini G.L., Panwar, D. and Singh, V. (2021), “Software reliability prediction of open source software using soft computing technique”, Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), vol. 14, no. 2, pp. 612–621, doi: https://doi.org/10.2174/2213275912666190307165332

Teuber, S. and Weigl, A. (2021), “Quantifying Software Reliability via Model-Counting”,in: Abate, A., Marin, A. (eds) Quantitative Evaluation of Systems. QEST 2021. Lecture Notes in Computer Science, vol 12846. Springer, Cham. doi: https://doi.org/10.1007/978-3-030-85172-9_4.

Nafreen, M., Bhattacharya, S. and Fiondella, L. (2020), “Architecture-based Software Reliability Incorporating Fault Tolerant Machine Learning”, Annual Reliability and Maintainability Symposium (RAMS), Palm Springs, CA, USA, pp. 1–6, doi: https://doi.org/10.1109/RAMS48030.2020.9153718.

Kuang, P., Zhao, Q.-M. and Xie, Z.-H. (2015), “Algorithms for solving unconctrained optimization problems”, 12th International Computer Conference on Wavelet Active Media Technology and Information Processing, pp. 379–382, doi: https://doi.org/10.1109/ICCWAMTIP.2015.7494013

Bayurskii, A. and Krepych, S. (2018), “Intelligent Syatem Analyzing Quality of Land Plots”, CEUR Workshop Proceedings 2300, pp.166-169, available at: https://ceur-ws.org/Vol-2300/Paper40.pdf

(2024), Breakpoints|intellij idea documentation, available at: https://www.jetbrains.com/help/idea/using-breakpoints.html

Kuchuk, N., Kashkevich, S., Radchenko, V., Andrusenko, Y. and Kuchuk, H. (2024), “Applying edge computing in the execution IoT operative transactions”, Advanced Information Systems, vol. 8, no. 4, pp. 49–59, doi: https://doi.org/10.20998/2522-9052.2024.4.07

Mezhuev, P., Gerasimov, A., Privalov, P. and Butkevich, V. (2021), “A dynamic algorithm for source code static analysis”, Ivannikov Memorial Workshop (IVMEM), pp.57-60, doi: https://doi.org/10.1109/IVMEM53963.2021.00016

Zhang, Y., Sun, Y., Si, G., Dong, B. and Chen, W. (2022), “An overview of source code static analysis method based on knowledge graph”, IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5, pp. 1772–1775, doi: https://doi.org/10.1109/IMCEC55388.2022.10019850

Spivak, I., Krepych, S., Litvynchuk, M. and Spivak, S. (2021), “Validation and data processing in json format”,

IEEE EUROCON 2021 19th International Conference on Smart Technologies, pp. 326–330, doi: https://doi.org/10.1109/EUROCON52738.2021.9535582

Krutko, V., Spivak, I. and Krepych, S. (2023), “An approach to assessing the reliability of software systems based on a graph model of method dependence”, 6th Worksop for Young Scientists in Computer Science & Software Engineering, pp. 37–47, available at: https://ceur-ws.org/Vol-3662/paper11.pdf