GenAI-Powered Test Case Generation for Microservices in CI/CD Pipelines
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I1P111Keywords:
Generative AI, Microservices, Test Case Generation, CI/CD, Federated Learning, Explainable AI, Software TestingAbstract
The adoption of microservice architectures has significantly accelerated software development velocity, yet it has introduced profound challenges in software testing. The distributed and independently deployable nature of microser- vices complicates the generation of comprehensive unit and integration test cases, often creating a bottleneck within Con- tinuous Integration/Continuous Deployment (CI/CD) pipelines. This paper introduces a novel framework, ”TestGenius,” that leverages Generative Artificial Intelligence (GenAI) to auto- mate and optimize test case generation for microservices. Our approach utilizes a multi-modal generative model that learns from API contracts (e.g., OpenAPI specifications) and runtime operational logs to produce semantically rich and contextually relevant test cases. To facilitate cross-organizational learning without compromising sensitive data, we propose a federated learning framework governed by a novel trust metric, ensuring the integrity and accountability of model contributions from different silos. Furthermore, we address the critical need for transparency in AI-driven testing by developing a framework to quantify and optimize the trade-off between the performance (i.e., test coverage, bug detection rate) and explainability of the generated tests. Our experimental design and hypothetical results demonstrate that TestGenius can significantly increase test coverage and fault detection rates compared to traditional test generation methods, paving the way for more resilient, efficient, and intelligent testing paradigms in modern software engineering
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