Received 05.11.2025, Revised 03.02.2026, Accepted 26.03.2026 Published 20.04.2026

Effectiveness of artificial intelligence for test prioritisation in distributed systems of Ukrainian and international software development

Andrii Zadorozhnii

The growing complexity of distributed Continuous Integration/Continuous Delivery (CI/CD) systems, and the limited scalability and stability of conventional heuristic methods for test prioritisation, necessitates the investigation of alternative methods of optimising the testing process. The purpose of this research was to determine the features of using AI methods for test prioritisation and to suggest an approach for integrating AI methods into automated testing processes. The research involved a comparative analysis of intelligent and hybrid methods for test prioritisation in distributed systems, using the APFD and APFDc metrics. The results of the study show the advantage of intelligent and hybrid approaches to test prioritisation over conventional heuristics. The random approach to test prioritisation proved to be the least efficient, achieving an APFD of approximately 0.51. More sophisticated heuristic approaches increased the APFD to around 0.62. Population-based methods increased the APFD to approximately 0.72. Using machine learning methods increased the APFD to about 0.76. The best results were achieved by using hybrid methods that combined machine learning and PSO. The APFD in this case reached 0.81, and the execution time for test suites decreased by nearly 45%. These results confirm that the integration of AI methods into the testing process is suitable for distributed CI/CD systems. The results of this study can be used by software developers, QA teams and engineers to optimise the testing processes in distributed systems

intelligent algorithms; machine learning; hybrid methods; optimisation algorithms; scalability; testing efficiency
125-139
Zadorozhnii, A. (2026). Effectiveness of artificial intelligence for test prioritisation in distributed systems of Ukrainian and international software development. Information Technologies and Computer Engineering, 23(1), 125-139. https://doi.org/10.31649/vitce/1.2026.125

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