AI Enhanced Software Testing and Continuous Integration for Secure Digital Payment Platforms with Big Data Analytics
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Abstract
The rapid growth of digital payment platforms has created an urgent need for robust, secure, and efficient software development practices. AI-enhanced software testing combined with Continuous Integration (CI) provides a transformative approach to ensuring the reliability, security, and scalability of digital payment systems. By leveraging machine learning algorithms and predictive analytics, AI-assisted testing automates error detection, optimizes test coverage, and accelerates defect resolution, reducing human intervention and testing time. Continuous Integration ensures seamless integration of code changes, enabling frequent, reliable, and secure software deployments while minimizing system downtime. Integration with big data analytics enables the processing of vast transactional datasets in real-time, enhancing fraud detection, anomaly identification, and operational intelligence. This research investigates the impact of AI-enhanced software testing, CI, and big data analytics on secure digital payment platforms, analyzing performance improvements, security enhancements, and operational efficiency gains. The study also evaluates challenges, including data privacy, scalability, and integration complexities. Findings indicate that combining AI, CI, and big data analytics provides a comprehensive framework for enhancing digital payment platform security, ensuring high system reliability, and enabling data-driven decision-making for both users and financial institutions.
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