Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines

TL;DR

This work addresses the inefficiency of static CI/CD pipelines by proposing a reinforcement learning approach that dynamically selects test scope for each commit. Formulated as a Markov Decision Process and implemented with a Deep Q-Network in a configurable CI/CD simulator, the method balances throughput and defect risk using a penalty parameter $\beta$ and discount $\gamma = 0.99$, achieving up to a 30% throughput gain and 25% test-time reduction while keeping defect misses below 5%. The contributions include the MDP formulation, a reusable simulator, and empirical evidence that adaptive policies selectively run full, partial, or no tests depending on contextual risk, with robustness under adversarial sequences. The results demonstrate practical potential for more efficient, resilient, and sustainable DevOps workflows, and the work discusses deployment strategies, limitations, and avenues for future improvement.

Abstract

Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern software delivery, yet their static workflows often introduce inefficiencies as systems scale. This paper proposes a reinforcement learning (RL) based approach to dynamically optimize CI/CD pipeline workflows. The pipeline is modeled as a Markov Decision Process, and an RL agent is trained to make runtime decisions such as selecting full, partial, or no test execution in order to maximize throughput while minimizing testing overhead. A configurable CI/CD simulation environment is developed to evaluate the approach across build, test, and deploy stages. Experimental results show that the RL optimized pipeline achieves up to a 30 percent improvement in throughput and approximately a 25 percent reduction in test execution time compared to static baselines, while maintaining a defect miss rate below 5 percent. The agent learns to selectively skip or abbreviate tests for low risk commits, accelerating feedback cycles without significantly increasing failure risk. These results demonstrate the potential of reinforcement learning to enable adaptive and intelligent DevOps workflows, providing a practical pathway toward more efficient, resilient, and sustainable CI/CD automation.

Figures (5)

• Figure 1: Flow diagram of the simulated CI/CD pipeline environment used for training and evaluation of the RL agent.
• Figure 2: Distribution of simulated commit risk scores used to train and evaluate the RL policy across varying CI/CD scenarios.
• Figure 3: Comparison of average pipeline time per commit. RL-optimized pipeline reduces latency by avoiding unnecessary tests.
• Figure 4: Average test time per pipeline execution. RL policy reduces test overhead while maintaining test effectiveness.
• Figure 5: Commits processed per hour (pipeline throughput). RL agent yields substantial gain by optimizing test execution.