Науковий вісник НГУ

Model for improving the reliability of microservice software during the functional testing phase

• 
• 

User Rating:  / 0

PoorBest 

Details

Parent Category: 2026

Category: Content №3 2026

Created on 26 June 2026

Last Updated on 26 June 2026

Published on 30 November -0001

Written by M. M. Seniv, O. M. Kuzmych

Hits: 1153

Authors:

M. M. Seniv, orcid.org/0000-0003-1044-4628, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. M. Kuzmych*, orcid.org/0000-0002-9749-6385, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (3): 146 - 154

https://doi.org/10.33271/nvngu/2026-3/146

Abstract:

Purpose. The development and justification of a new testing model aimed at improving the reliability of software systems built using microservice architecture. The proposed model systematically takes into account the specifics of distributed systems, the characteristics of microservice architecture, and fault tolerance aspects that are insufficiently covered by classical testing approaches.

Methodology. A six-layer “Pentagon” testing model is proposed. The limitations of existing models (“testing pyramid” and “testing honeycomb”) in the context of cloud-native systems were analyzed. A combined validation approach was applied: a survey of IT professionals to identify the importance of each testing layer and comparative load testing of two system versions ‒ the standard one and the one enhanced with the “Pentagon” testing model. Key reliability metrics were evaluated, including Failure Rate, Availability, MTBF, etc.

Findings. The study resulted in the formalization of the “Pentagon” testing model, which consists of six sequential layers. Evaluation criteria were developed to quantitatively assess the contribution of each layer to overall system reliability. Expert feedback confirmed the higher importance of the Fault-Tolerance, End-to-End, and Global Integration Testing layers compared to Unit Testing. Experimental results demonstrated that the system tested using the proposed model exhibited higher reliability under failure conditions: the number of failures decreased by 9.9 %, MTBF increased by 9.5 %, and Availability improved by 2 %.

Originality. For the first time, a conceptual “Pentagon” model has been developed, which, unlike the existing ones, structures the microservice software testing process into six layers. A unique feature of the model is the isolation of fault tolerance verification and resilience to chaotic infrastructure failures into separate mandatory stages, allowing for the formalization of these processes. Based on the developed model, an approach to functional software testing for reliability improvement is proposed for the first time, which involves the systemic integration of the aforementioned testing levels and Chaos Engineering into the Software Development Lifecycle (SDLC). Unlike classical approaches, such integration ensures a comprehensive improvement in software reliability indicators. A method for evaluating the importance of testing layers based on five weighted criteria has been developed, allowing for an objective assessment of the importance and scope of each model layer in the software testing strategy.

Practical value. The model provides a structured foundation for forming a testing strategy for microservice applications, facilitates early detection of architectural flaws, reduces the risks of failures in production, and lowers the costs of their remediation.

Keywords: software, microservice architecture, functional testing, “Pentagon” testing model, “chaos testing”

References.

1. ISO/IEC (2023). Systems and software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) – Product quality model (ISO/IEC 25010:2023). Retrieved from https://www.iso.org/obp/ui/#iso:std:iso-iec:25010:ed-2:v1:en

2. Anand, V., Garg, D., Kaufmann, A., & Mace, J. (2023). Blueprint: A Toolchain for Highly-Reconfigurable Microservice Applications. SOSP ‘23: 29 ^th Symposium on Operating Systems Principles. ACM. https://doi.org/10.1145/3600006.3613138

3. Baboi, M., Iftene, A., & Gîfu, D. (2019). Dynamic Microservices to Create Scalable and Fault Tolerance Architecture. Procedia Computer Science, 159, 1035-1044. https://doi.org/10.1016/j.procs.2019.09.271

4. Waseem, M., Liang, P., Shahin, M., Di Salle, A., & Márquez, G. (2021). Design, monitoring, and testing of microservices systems: The practitioners’ perspective. Journal of Systems and Software, 182, 111061. https://doi.org/10.1016/j.jss.2021.111061

5. Waseem, P., & Kaur, P. (2018, December). A survey of fault tolerance in cloud computing. Journal of King Saud University – Computer and Information Sciences, 33(10), 1159-1176. https://doi.org/10.1016/j.jksuci.2018.09.021

6. Molchanov, H., & Zhmaiev, A. (2018). Circuit breaker in systems based on microservices architecture. Advanced Information Systems, 2(4), 74-77. https://doi.org/10.20998/2522-9052.2018.4.13

7. Sambir, A., Yakovyna, V., & Seniv, M. (2017). Recruiting software architecture using user generated data. 2017 XIII ^th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE. https://doi.org/10.1109/memstech.2017.7937557

8. Falahah, Surendro, K., & Sunindyo, W. D. (2021). Circuit Breaker in Microservices: State of the Art and Future Prospects. IOP Conference Series: Materials Science and Engineering, 1077(1), 012065. https://doi.org/10.1088/1757-899x/1077/1/012065

9. Mohammadian, V., Navimipour, N. J., Hosseinzadeh, M., & Darwesh, A. (2022). Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review. IEEE Access, 10, 12714-12731. https://doi.org/10.1109/access.2021.3139730

10.      Bouizem, Y., Bouanani, N., & Reddad, A. (2023). Integrating request replication into FaaS platforms: an experimental evaluation. Journal of Cloud Computing, 12, 45. https://doi.org/10.1186/s13677-023-00457-z

11.      Kuzmych, O. M., Lakhai, V. Y., & Seniv, M. M. (2023). Improved approach to automated software integration testing under the conditions of serverless architecture. Scientific Bulletin of UNFU, 33(3), 97-101. https://doi.org/10.36930/40330314

12.      Simonsson, J., Zhang, L., Morin, B., Baudry, B., & Monperrus, M. (2021). Observability and chaos engineering on system calls for containerized applications in Docker. Future Generation Computer Systems, 122, 117-129. https://doi.org/10.1016/j.future.2021.04.001

13.      Lakhai, V., Kuzmych, O., & Seniv, M. (2023). An improved method for increasing maintainability in terms of serverless architecture application. 2023 IEEE 18 ^th International Conference on Computer Science and Information Technologies (CSIT). IEEE. https://doi.org/10.1109/csit61576.2023.10324273

14.      Basiri, A., Behnam, N., de Rooij, R., Hochstein, L., Kosewski, L., Reynolds, J., & Rosenthal, C. (2016). Chaos Engineering. IEEE Software, 33(3), 35-41. https://doi.org/10.1109/ms.2016.60

15.      Reghenzani, F., Guo, Z., & Fornaciari, W. (2023). Software Fault Tolerance in Real-Time Systems: Identifying the Future Research Questions. ACM Computing Surveys. https://doi.org/10.1145/3589950

16.      Kuzmych, O., & Seniv, M. (2024). An Improved Approach to Increase the Fault Tolerance of Microservice Software Through Automated Functional and Fault Tolerance Testing. 2024 IEEE 19 ^th International Conference on Computer Science and Information Technologies (CSIT), (pp. 1-4). IEEE. https://doi.org/10.1109/csit65290.2024.10982594

17.      Patil, P. S. N. (2022). Estimation of Mean Time between Failures of a Two Tandem Mill of a Sugar Plant. International Journal for Research in Applied Science and Engineering Technology, 10(1), 474-478. https://doi.org/10.22214/ijraset.2022.39823

18.      Wang, J., & Yin, H. (2019). Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis. IEEE Access, 7, 85298-85309. https://doi.org/10.1109/access.2019.2926159

19.      Bartok, T., Hejja, F., & Kocsis, G. (2024). Availability Modeling in Software Architecture. 2024 IEEE 3 ^rd Conference on Information Technology and Data Science (CITDS), (pp. 1-6). IEEE.
https://doi.org/10.1109/citds62610.2024.10791390

20.      Vogel, M., Knapik, P., Cohrs, M., Szyperrek, B., Pueschel, W., Etzel, H., Fiebig, D., Rausch, A., & Kuhrmann, M. (2020). Metrics in automotive software development: A systematic literature review. Journal of Software: Evolution and Process, e2296. https://doi.org/10.1002/smr.2296

• < Prev
• Next >