The goal is to establish an adaptive testing framework that autonomously corrects script failures in response to changes in the application under test (AUT). Leveraging Healenium and machine learning algorithms enhances resilience, reduces maintenance efforts, and improves the overall efficiency of automated testing suites. The technology stack includes Selenium for web automation, Healenium for healing, and Java-based machine learning libraries. The integration process is detailed in a step-by-step guide, showcasing Healenium's proficiency in identifying and rectifying changes in element locators. The machine learning component significantly contributes to predicting and adapting to changes within the AUT, explored in the project's investigation of the training dataset, features, and selected algorithms.
The self-healing mechanism autonomously detects script failures, initiates Healenium for recovery, and employs machine learning for informed corrections. This approach boosts testing framework adaptability, diminishing manual intervention and enhancing script reliability. The system continually learns from test executions, fostering adaptive learning and refining capabilities to handle dynamic changes effectively over time. Real-world case studies will illustrate the practical impact of the autonomous healing system, supported by metrics such as reduced maintenance time, increased script stability, and improved test coverage.