Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization

2025-08-11

AI Summary

In large monorepos, CI merge pipelines under high load become integration bottlenecks, severely impeding development velocity. To address this, we propose a build-system-agnostic optimization framework: leveraging historical build logs, PR metadata, and contextual features, we train a lightweight model to predict per-PR build success probability; based on these predictions, we dynamically prioritize pull requests—scheduling high-probability requests first during peak loads. Our approach requires no modifications to underlying CI infrastructure, ensuring strong integrability and deployment feasibility. Evaluated on a real-world large-scale production monorepo, it achieves significantly higher throughput compared to FIFO and non-learning baselines, effectively alleviating CI integration bottlenecks. The method provides a practical, scalable optimization pathway for high-concurrency software delivery without compromising system compatibility or operational simplicity.

Technology Category

• Machine Learning: Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Integrating changes into large monolithic software repositories is a critical step in modern software development that substantially impacts the speed of feature delivery, the stability of the codebase, and the overall productivity of development teams. To ensure the stability of the main branch, many organizations use merge pipelines that test software versions before the changes are permanently integrated. However, the load on merge pipelines is often so high that they become bottlenecks, despite the use of parallelization. Existing optimizations frequently rely on specific build systems, limiting their generalizability and applicability. In this paper we propose to optimize the order of PRs in merge pipelines using practical build predictions utilizing only historical build data, PR metadata, and contextual information to estimate the likelihood of successful builds in the merge pipeline. By dynamically prioritizing likely passing PRs during peak hours, this approach maximizes throughput when it matters most. Experiments conducted on a real-world, large-scale project demonstrate that predictive ordering significantly outperforms traditional first-in-first-out (FIFO), as well as non-learning-based ordering strategies. Unlike alternative optimizations, this approach is agnostic to the underlying build system and thus easily integrable into existing automated merge pipelines.

Problem

• Optimizing PR order in merge pipelines to reduce bottlenecks
• Predicting build success using historical data and PR metadata
• Increasing merge pipeline throughput during peak hours

Innovation

• Prioritize PRs using historical build data
• Dynamic ordering based on build success likelihood
• Build system agnostic merge pipeline optimization