DOI: https://doi.org/10.36719/2789-6919/45/253-257
Jamil Aliyev
Azerbaijan State Oil and Industry University
Master student
https://orcid.org/0009-0002-6409-1856
jamilaliyev42@gmail.com
A Conceptual Framework for Adaptive Ci/Cd Converyors Optimization Via Deep Reinforcement Learning
Abstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines are essential for modern software delivery, yet their optimization presents ongoing challenges due to dynamic conditions and inherent complexity. Static configurations struggle to adapt efficiently to variations in code changes, resource availability, and testing needs. This paper proposes a conceptual framework utilizing Deep Reinforcement Learning (DRL) to enable adaptive orchestration of CI/CD pipeline stages. We articulate the conveyors optimization problem as a sequential decision-making process amenable to RL techniques. A DRL agent, under this framework, would learn optimal policies for dynamic task scheduling, resource allocation, and predictive test selection by interacting with the conveyors environment, guided by a reward function balancing efficiency and quality. The proposed approach aims to overcome the limitations of static and simple heuristic methods by leveraging the learning capabilities of DRL to continuously refine pipeline execution strategies based on observed states and outcomes. This research posits that such adaptive, learning-based systems represent a promising direction for significantly enhancing the performance and responsiveness of software delivery pipelines.
Keywords: continuous integration, uninterrupted delivery, CI/CD optimization, deep reinforcement learning, software engineering, DevOps, conveyor crchestration, conceptual framework