Methodology for Continuous Performance Testing of Educational Information Systems Based on k6 and Dynamic Quality Gates

Анотація

Educational information systems operate under uneven and often unpredictable workloads caused by examination periods, mass access to learning materials, online assessment activities, and administrative operations. Traditional episodic performance testing is often insufficient for such systems because performance regressions may appear during continuous software delivery and remain undetected until deployment. This paper proposes a methodology for continuous performance testing of educational information systems based on k6 and dynamic Quality Gates. The methodology combines realistic educational workload scenarios, automated execution of tests in a CI/CD pipeline, baseline-oriented regression detection, and release validation using service-level objectives. A low-cost experimental environment is proposed to make the approach reproducible for universities and small development teams. The environment may be implemented on a single virtual machine or free-tier cloud instance and is intended primarily for regression-oriented validation rather than full-scale capacity testing. The results show that k6-based continuous performance testing can support early detection of performance degradation, improve release decision-making, and provide a practical path for gradual implementation of performance quality control in educational digital ecosystems.

The study shows that performance testing of educational systems should be based on realistic user scenarios rather than only on generic technical request patterns. Critical workflows such as authentication, course access, online test start, answer submission, and test completion should be represented as repeatable k6 scenarios with clearly defined metrics and acceptance criteria.

A key feature of the proposed methodology is the use of dynamic Quality Gates that combine thresholds, baselines, and service-level objectives. This makes it possible to detect both absolute violations of performance requirements and relative degradation compared with previously accepted behavior. As a result, performance testing becomes not only a diagnostic activity, but also a release control mechanism.

References

1.        Humble, J. and Farley, D. (2010) Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Boston: Addison-Wesley.

2.        Gregg, B. (2020) Systems Performance: Enterprise and the Cloud. 2nd edn. Boston: Addison-Wesley.

3.        Newman, S. (2021) Building Microservices: Designing Fine-Grained Systems. 2nd edn. Sebastopol: O’Reilly Media.

4.        Heinrich, R., van Hoorn, A., Knoche, H., Li, F., Lwakatare, L.E., Pahl, C., Schulte, S. and Wettinger, J. (2017) Performance Engineering for Microservices: Research Challenges and Directions, in Companion of the 8th ACM/SPEC International Conference on Performance Engineering. DOI: 10.1145/3053600.3053653.

5.        Camilli, M., Guerriero, A., Janes, A., Russo, B. and Russo, S. (2022) Microservices Integrated Performance and Reliability Testing, in Proceedings of the IEEE/ACM International Conference on Automation of Software Test. DOI: 10.1145/3524481.3527233.

6.        Vasilevskii, A., Kachur, O., Balsamo, S., Knottenbelt, W., Abad, C. and Shang, W. (2024) Self-Service Performance Testing Platform for Autonomous Development Teams, in Companion of the 15th ACM/SPEC International Conference on Performance Engineering. DOI: 10.1145/3629527.3652268.

7.        Santos, L.B.R., de Souza, E.F., Endo, A.T., Trubiani, C., Pinciroli, R. and Vijaykumar, N.L. (2025) Performance regression testing initiatives: A systematic mapping, Information and Software Technology, 179, article 107641. DOI: 10.1016/j.infsof.2024.107641.

8.        Fischer, S., Urbanke, P., Ramler, R., Steidl, M. and Felderer, M. (2024) An Overview of Microservice-Based Systems Used for Evaluation in Testing and Monitoring: A Systematic Mapping Study, in Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test, pp. 1–11. DOI: 10.1145/3644032.3644445.

9.        Ramu, V.B. (2023) Optimizing DevOps Pipelines with Performance Testing: A Comprehensive Approach, International Journal of Computer Trends and Technology, 71(6), pp. 35–41. DOI: 10.14445/22312803/IJCTT-V71I6P106.

10.    Kovalenko, O.O. and Palamarchuk, Ye.A. (2020) Dynamic results of stress quarantine testing of the JetIQ VNTU learning management system and G Suite for Education services’, in Internet-Education-Science 2020: Proceedings of the XII International Scientific and Practical Conference, pp. 157–158.

11.    Storozhuk, Yu.V. and Kovalenko, O.O. (2025) Method for determining target performance indicators of software, Visnyk Vinnytskoho Politekhnichnoho Instytutu, 4, pp. 108–117. DOI: 10.31649/1997-9266-2025-181-4-108-117.