INTELLIGENT APPROACH TO ENHANCING THE STABILITY OF AUTOMATED TESTING SYSTEMS BASED ON REINFORCEMENT LEARNING

Abstract

This research develops and substantiates an intelligent reinforcement-based architecture aimed at enhancing the stability of automated testing systems, using Selenium as a practical foundation. The study identifies the root causes of instability in UI testing – DOM volatility, asynchronous rendering, network latency, and rigid synchronization – and formulates a multilayered adaptive model that mitigates these factors through reinforcement learning mechanisms. The proposed Intelligent Reinforcement Layer restructures Selenium’s classical hierarchy, namely IDE, Client–Server, and Grid, into a unified self-learning framework capable of autonomous adjustment to environmental drift. Within the IRL, the IDE Reinforcement Agent performs local adaptation by regulating playback timing and locator selection in response to structural changes in the DOM. The Client–Server Adaptive Layer, comprising the Execution Stability Agent and Hierarchical Locator Adaptation Agent, ensures execution stability by adjusting synchronization intervals, wait policies, and locator recovery strategies based on real-time telemetry. At the distributed level, the Grid Learning Environment applies cooperative multi-agent learning to balance test workloads across nodes, optimizing system-wide stability and throughput. The research formalizes unified reinforcement-learning logic, defines state–action structures for each subsystem, and introduces a collective experience buffer for cross-layer training. Comparative analysis demonstrates that while traditional frameworks such as Cypress and Playwright implement fixed stabilization procedures, the proposed IRL architecture achieves adaptive resilience through continuous learning and feedback. The study concludes that integrating reinforcement learning into Selenium’s architecture yields a self-optimizing testing environment capable of maintaining long-term stability, reducing flakiness, and improving the robustness of automated QA processes under evolving application conditions.