Paper Title
Test Automation for Cloud-Native Applications: Challenges and Solutions

Abstract
Cloud-native applications, characterized by their distributed, microservices-based architectures deployed on dynamic containerized platforms like Kubernetes, present unique challenges for traditional test automation. These challenges include the complexity of managing numerous interconnected services, the ephemeral nature of containers, the need for rapid and frequent deployments within CI/CD pipelines, and the difficulty of replicating production environments for testing. This research paper explores these challenges in detail, examining the limitations of existing testing approaches when applied to cloud-native environments. The paper proposes a comprehensive set of solutions and best practices, encompassing design patterns such as Behavior-Driven Development, Keyword-Driven Testing, and parallel execution, as well as architectural considerations like modularity, abstraction layers, and pluggable components. Additionally, the paper discusses the integration of advanced techniques such as AI and machine learning for adaptive reporting and test generation, continuous testing within the CI/CD pipeline, and cross-platform test execution. By adopting the strategies outlined in this research, development teams can build robust, scalable, and efficient test automation frameworks that effectively address the complexities of cloud-native applications and ensure software quality in dynamic cloud environments. It also discusses cloud native testing (Lopes, 2023; The Challenges of Testing in Your CI/CD Pipeline, 2024) and challenges faced in implementing them (Blanco-Cuaresma et al., 2019; Lopes, 2022, 2023).