Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines

2025-12-20

International Conference on Computational Intelligence and Communication Networks

AI Summary

This work addresses the inefficiency of traditional static CI/CD workflows in adapting to varying commit risks during system scaling. It introduces the first formulation of CI/CD pipelines as a Markov decision process and proposes a reinforcement learning–based dynamic test scheduling approach that enables runtime-adaptive test selection. The method significantly enhances pipeline efficiency while maintaining high defect detection quality, achieving a defect miss rate below 5%. Compared to static baselines, it improves throughput by up to 30% and reduces test execution time by approximately 25%. This study establishes the first reinforcement learning–driven dynamic decision framework for optimizing CI/CD pipelines, offering a principled and scalable solution to adaptive testing in continuous integration environments.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Continuous Integration and Deployment (CI/CD) pipelines are core to modern software delivery, but their static workflows can be inefficient. This paper proposes a reinforcement learning (RL) approach to optimize CI/CD pipeline workflows dynamically. We model the pipeline as a Markov Decision Process and train an RL agent to make runtime decisions (e.g., selecting test scope) that maximize throughput while minimizing testing overhead. A simulated CI/CD environment with configurable build, test, and deploy stages is developed to evaluate the approach. Experimental results show that the RL-optimized pipeline achieves up to a 30 % improvement in throughput and about a 25 % reduction in test execution overhead compared to a static baseline. The agent learns to skip or abbreviate certain tests when appropriate, accelerating delivery without significantly increasing the risk of undetected failures. This work demonstrates the potential of RL to adapt DevOps workflows for greater efficiency, providing novel insights into intelligent pipeline automation.

Problem

• CI/CD pipelines
• workflow optimization
• static workflows
• testing overhead
• throughput inefficiency

Innovation

• Reinforcement Learning
• CI/CD Optimization
• Dynamic Workflow
• Markov Decision Process
• Test Selection