Enhancing Continuous Integration Pipelines with AI-Powered Risk Assessment for Code Merging in Large Repositories
Keywords:
continuous integration, AI-powered risk assessment, code merging, large repositories, machine learning, software development, merge conflict detection, automated testingAbstract
Continuous integration (CI) pipelines play a vital role in modern software development by ensuring code quality and fostering agile practices. However, in large repositories, managing code merging, detecting risks, and ensuring the stability of the build remain challenging tasks. This paper explores the integration of artificial intelligence (AI) into CI pipelines to enhance risk assessment during code merging processes. By leveraging AI techniques such as machine learning (ML) and natural language processing (NLP), we propose an AI-powered risk assessment framework that improves code quality, detects potential merge conflicts, and reduces the likelihood of introducing bugs or security vulnerabilities. We further discuss the practical application of AI in automating risk assessment, providing real-time insights to developers, and fostering better decision-making in large repositories. The paper also reviews case studies demonstrating the effectiveness of this approach and suggests future directions for expanding AI integration within CI pipelines.
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