CA Lisa - CI CD CT Micro Service Test Implementation Guide

Ram Aurovind

Abstract: The CI CD CT Automation-Project reflects a timely and well-structured response to a persistent challenge within the international shipping ecosystem delivering a seamless, transparent, and intelligent customer experience across a fragmented logistics chain. What stands out is its use of cutting-edge technologies like Big Data, Microservices, and Machine Learning not merely as buzzwords, but as working components that power real-time classification of commodities, automate customs estimation, and streamline transit projections. This isn't just about technical efficiency it?s about rebuilding trust in data accuracy and turnaround times for shipping clients, who often feel lost in outdated systems. That said, the integration of smoke and regression testing within a Jenkins-based CI/CD pipeline adds a much-needed layer of resilience, ensuring that deployments across development, QA, staging, and production environments remain error-free and predictable. It is evident that this project is not only a technical upgrade but a strategic step toward reshaping how global trade tools interact with complex regulatory frameworks. The consistent auto-triggering, environment-specific validations, and intelligent automation mechanisms present a solid blueprint for future digital transformation initiatives in logistics tech. From my perspective, this initiative paves the way for other sectors to rethink automation not as a support function - but as a driver of customer satisfaction and operational clarity.

Keywords: commodity classification, big data logistics, CI/CD automation, shipping analytics, customer experience in trade

How to Cite?: Ram Aurovind, "CA Lisa - CI CD CT Micro Service Test Implementation Guide", Volume 14 Issue 6, June 2025, International Journal of Science and Research (IJSR), Pages: 1655-1660, https://www.ijsr.net/getabstract.php?paperid=SR25623221815, DOI: https://dx.doi.org/10.21275/SR25623221815