Pipeline Throughput Optimization in Continuous Integration Systems Using Data-Driven Feedback Loops

Abstract

Development is slowed by pipeline traffic and build errors in modern software engineering. Traditional CI optimization ignores changing build workloads, resource congestion, and test execution patterns with static or ad hoc scheduling. Data-driven feedback loops analyze build analytics, identify performance bottlenecks, and dynamically alter pipeline scheduling, resource allocation, and test execution priorities in this research. Real-time measurements and predictive modeling improve throughput and decrease failures via adaptive decision-making. Formalizing CI pipeline feedback loops, building resource and test optimization prediction models, and assessing workload throughput increases are contributions. We address system scalability, latency overhead, and build environment integration architectural trade-offs. Studies show pipeline efficiency may reduce software delivery times, operational costs, and self-optimizing CI systems.