Architectural Framework for AI-Driven Integrated Testing Pipelines in Modern Software Development Environments
DOI:
https://doi.org/10.15662/IJEETR.2025.0704015Keywords:
AI-driven testing, integrated testing pipelines, software development, machine learning, continuous integration, test automation, software qualityAbstract
Continuous testing and integration of software has become critical in the modern software development in order to provide quality and stability of the application. The growing complexity of software systems and the necessity to cause deployment cycle are some of the factors that require more efficient and automated test procedure. This study provides an Architectural Framework of AI-Driven Integrated Testing Pipelines that uses artificial intelligence (AI) to improve the process of testing at various phases of software development. The suggested structure will incorporate AI methods in the process of multiple testing processes, including but not limited to the generation of test cases, defect prediction, automated testing, and analysis of results, which will secure a more flexible and efficient testing pipeline. The structure will be compatible with the contemporary software development ecosystem, such as the microservices architecture and the cloud-computing product. Using machine learning algorithms and AI-driven analytics, the framework will minimize manual operation, enhance test coverage, and formulate a better defect-detection accuracy, therefore, it will enhance the release cycle without affecting the quality of the software. Also, the framework includes a feedback loop, which will allow learning and refining the process of testing due to the past data and the results of the tests continuously. The actual implementation of the suggested method is tested in the framework of the series of case studies related to the real-life development conditions that prove the efficiency of the production procedures to arrange the testing pipelines as optimally as possible and improve the overall quality of the software. The present paper is part of the increasing literature on AI in the field of software engineering, as it explains a holistic solution to the need to introduce AI-based testing into the contemporary development processes.
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