Continuous Integration at Scale: Reducing Build Failure Rates Through Predictive Pipeline Orchestration

Abstract

Frequent build failures in large monorepos impose significant engineering overhead. This study introduces a machine-learning-driven CI orchestrator that predicts failure-prone changesets and reallocates compute resources proactively. Evaluated across 14 enterprise repositories with over 80,000 daily commits, our approach reduced mean build failure rates by 34% and cut median pipeline latency by 22%. A gradient-boosted classifier trained on historical commit metadata, test coverage deltas, and dependency graphs drives scheduling decisions. Results demonstrate that predictive orchestration significantly improves developer throughput without requiring changes to existing toolchains.