Integrating machine learning algorithms into Oracle ERP testing pipelines: Enhancing accuracy and efficiency

Testing Oracle Enterprise Resource Planning (ERP) systems for reliable business operations has become essential to enterprises' increasing dependency on these systems. The traditional Enterprise Resource Planning systems test methodologies employ either manual testing or static automation tools with limited adjustment ability and anticipate capabilities. The research explores how machine learning algorithms can be added to Oracle ERP testing operations to boost testing quality and test survey extent alongside operational effectiveness. This study integrates supervised learning with unsupervised learning and reinforcement learning for adaptive test case optimization as its hybrid methodology. The evaluation models and training processes utilized test data from Oracle ERP modules for finance, supply chain, and human resources domains. A benchmark assessment of the proposed testing pipeline that integrates ML happened with traditional testing methods through measurement of defect discovery rate and execution time and precision together with test coverage metrics. The reported results show substantial enhancement in testing results, with accuracy increases reaching 35% and a 40% decrease in test execution time while showing improved resource effectiveness. ML integration led to the automatic automation of redundant testing procedures while simultaneously implementing predictive analysis functions, which detected possible failure points beforehand. Based on the results, intelligent automation in ERP testing shows enormous potential, establishing a flexible strategy for implementing ML-based testing infrastructure in modern enterprises.