Self-Healing Test Automation Framework using AI and ML

Abstract

Purpose: In the lifecycle of Product Development and Management, automated testing has become a cornerstone for ensuring product quality and accelerating release cycles. However, the maintenance of test automation suites often presents significant challenges, particularly due to the frequent changes in application interfaces that lead to broken tests. This paper explores the development and implementation of self-healing test automation frameworks that leverage Artificial Intelligence (AI) and Machine Learning (ML) techniques to automatically detect, diagnose, and repair broken tests. Methodology: By integrating AI/ML models capable of dynamic locator identification, intelligent waiting mechanisms, and anomaly detection, these frameworks can significantly reduce the maintenance burden associated with automated testing. The paper presents a comprehensive architecture of a self-healing test automation framework, detailing the AI/ML techniques employed and the workflow of the self-healing process. A real-world case study is included to demonstrate the practical application and benefits of the proposed framework. Findings: Evaluation results show substantial improvements in test suite reliability and reductions in maintenance time and costs. The AI/ML techniques used in the framework, such as dynamic locator identification and intelligent waiting mechanisms, proved effective in reducing the maintenance burden and improving the robustness of automated testing processes. Unique Contribution to Theory, Practice and Policy: This paper aims to provide insights into the potential of self-healing test automation frameworks to enhance the robustness and efficiency of automated testing processes. By adopting these frameworks, organizations can achieve more resilient and maintainable test automation strategies, ultimately contributing to higher product quality and faster release cycles.