Predictive Analytics and Self-Healing Systems Using Artificial Intelligence for Software Maintenance

Using artificial intelligence (AI), this study investigated real-time fault prediction and recovery in order to create a framework for software Maintenance and self-healing system. The majority of automation testing frameworks require assistance in anticipating and effectively recovering from errors, despite the expanding significance of software quality. In order to improve the resilience and adaptability of testing frameworks through self-healing mechanisms, this study makes use of AI, particularly machine learning algorithms. The first step in this study is a comprehensive analysis of the constraints in the testing systems and automation testing procedures already in use. The use of AI in software testing is then demonstrated, along with various viewpoints on how machine learning might improve the precision of fault detection and prediction. AI is being investigated in conjunction with predictive maintenance plans and data analysis techniques, which enable the early but accurate identification and avoidance of issues. We concentrate on creating automated recovery procedures that dynamically modify tests based on data collected in real time. To demonstrate real-world uses of self-healing algorithms in various software settings, case studies are provided. Thus, the study identifies implementation hurdles (economic organizational) and technical challenges (such AI integration and scalability). This study offers useful suggestions for implementing AI-driven self-healing frameworks with significant ramifications for industry stakeholders. Lastly, the prospect of an automated future is discussed in this paper's conclusion.