Generative AI for Automated Software Validation in Cloud-Native Enterprise Environments
Main Article Content
Abstract
Cloud-native enterprise applications are increasingly developed as distributed ecosystems consisting of microservices,
containers, application programming interfaces, serverless functions, databases, and dynamically provisioned infrastructure.
Although these architectures provide scalability and flexibility, they substantially increase the complexity of software
validation. Conventional validation techniques often depend on manually designed test cases, predefined rules, static
scripts, and extensive human intervention, making them difficult to scale across rapidly changing cloud environments.
Generative Artificial Intelligence (GenAI) provides an emerging approach for improving automated software validation by
generating test cases, creating test data, identifying potential defects, analyzing logs, predicting failure conditions, and
recommending corrective actions. This paper examines the application of GenAI for automated validation in cloud-native
enterprise environments. It proposes a methodology that integrates large language models, automated test generation,
continuous integration and continuous delivery pipelines, observability data, and feedback-driven validation. The approach
emphasizes functional, integration, API, security, performance, and resilience testing while maintaining human oversight
for critical validation decisions. The study also considers the advantages and disadvantages of GenAI-based validation,
including improved test coverage, reduced testing effort, faster defect detection, adaptability, and scalability, alongside
challenges related to hallucination, reliability, security, explainability, data privacy, and computational cost. The proposed
framework demonstrates how GenAI can complement conventional software testing practices and contribute to more
intelligent, continuous, and adaptive validation of enterprise cloud-native systems.
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References
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