PYTHON-BASED INTELLIGENT TEST AUTOMATION FOR ENHANCING RELIABILITY OF ENTERPRISE DIGITAL PLATFORMS IN SAUDI ARABIA

In Saudi Arabia, enterprise digital platforms are being required to produce rapid releases without compromising reliability, security, or user trust. The pressure to do this is making it harder to keep conventional regression strategies going, since large test suites, distributed services, mobile interfaces, and frequent integrations result in feedback delays and unreliable quality signals. This article looks at how intelligent test automation based on Python can improve reliability assurance by using executable automation together with data-driven prioritization, defect prediction, adaptive test generation, flaky-test detection, and feedback from continuous delivery. A critical review of 30 peer-reviewed studies published between 2020 and 2025 was carried out,and each of the sources included was checked using a bibliographic record or publisher that provides a DOI. The evidence was organised around four areas: automation architecture, intelligence mechanism, reliability outcome, and the organisational deployment context. The literature shows that higher levels of automation maturity are linked to better product quality and shorter release cycles, and that machine learning can enhance the value of regression selection, prioritization, failure prediction, and the management of unstable tests [1,6-13,20-27]. Nevertheless, intelligent methods are still vulnerable to problems relating to data quality, concept drift, explainability, maintenance cost, and the risk of optimising proxy metrics instead of business-critical reliability.