Self-Healing Test Automation Framework Using Autonomous ML Agents for Real-Time Test Maintenance and Failure Recovery
Keywords:
Self-healing automation, autonomous agents, machine learning, test maintenance, failure recovery, dynamic test execution, intelligent test automation, real-time software testing, reinforcement learning.Abstract
As software systems evolve rapidly, maintaining automated test suites has become increasingly difficult. Frequent code changes often break test scripts, leading to unreliable test results and rising maintenance costs. This paper introduces a self-healing test automation framework that uses machine learning (ML) agents to adapt to changes in real time. By combining anomaly detection, flexible locator strategies, and reinforcement learning, the system can automatically identify and fix broken tests without manual intervention. It’s designed to reduce flaky tests, speed up recovery from failures, and keep test coverage stable even in fast-changing environments. Our experiments using industry-standard applications show a 38% drop in manual test maintenance and a 45% boost in test execution stability. The framework moves us closer to truly autonomous testing systems that can learn, adapt, and grow with the software they test.
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