Automating Deployment Pipelines with AI-Driven Intelligent Build Optimization
Abstract
The growing complexity of enterprise software delivery ecosystems, characterized by monorepo codebases spanning millions of lines of code, multi-module Maven project hierarchies with hundreds of interdependent artifacts, and organizational demands for continuous integration and deployment at cadences measured in hours rather than days, has exposed fundamental scalability limitations in conventionally configured Jenkins pipeline architectures that rely on static stage sequencing, fixed agent pool allocation, and reactive failure handling mechanisms insufficient for the throughput and reliability expectations of modern DevOps organizations. This paper proposes a comprehensive AI-driven build optimization framework that augments Maven and Jenkins-based deployment pipelines with an intelligent orchestration layer capable of predicting build durations, dynamically parallelizing pipeline stages, proactively identifying failure-prone change sets before execution, optimizing test suite selection through historical flakiness analysis, and autonomously right-sizing Jenkins agent resource allocations in response to observed and forecast pipeline demand. The framework employs a gradient-boosted regression ensemble for per-build duration and resource consumption forecasting, a graph neural network for Maven module dependency impact analysis and selective build scoping, and a Deep Q-Network reinforcement learning agent for end-to-end pipeline scheduling optimization across heterogeneous Jenkins agent pools. Experimental evaluation on an enterprise Java platform comprising 312 Maven modules and processing 847 pipeline executions per day demonstrates that the proposed AI-optimized pipeline architecture achieves a 63.6% reduction in average full build duration, an 84.4% reduction in pipeline failure rate, a 356.3% increase in daily deployment frequency, and a 41.8% reduction in monthly CI/CD infrastructure cost compared to a conventionally configured baseline deployment, validating the transformative potential of AI-driven intelligent build optimization for enterprise-scale continuous delivery systems.
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