ARTIFICIAL INTELLIGENCE–BASED OPTIMIZATION OF CI/CD PIPELINES FOR REDUCED BUILD FAILURES AND DEPLOYMENT TIME
Keywords:
Artificial Intelligence; Continuous Integration; Continuous Deployment; DevOps; CI/CD Pipeline Optimization; Machine Learning; Build Failure Prediction; Software Delivery Automation; Pipeline Performance Optimization; Intelligent DevOps SystemsAbstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines play a central role in modern DevOps practices by enabling automated software development, testing, and deployment. However, traditional rule-based pipeline configurations often encounter challenges such as build failures, inefficient resource allocation, prolonged deployment times, and operational bottlenecks, which can negatively affect software delivery performance. The integration of artificial intelligence (AI) and machine learning techniques within DevOps environments has recently emerged as a promising approach to optimize CI/CD pipelines and enhance software delivery efficiency. This study aimed to evaluate the effectiveness of AI-based optimization techniques in reducing build failures and deployment time within CI/CD workflows.
A quantitative experimental design was employed using a simulated DevOps environment consisting of 420 pipeline execution records. Traditional rule-based pipelines were compared with AI-optimized pipelines utilizing machine learning algorithms including Random Forest, Support Vector Machine, and Gradient Boosting models for failure prediction and workflow optimization. Key performance indicators analyzed included build failure rate, average build time, deployment time, and pipeline throughput. The results demonstrated that AI-driven optimization significantly improved pipeline performance. The build failure rate decreased from 21.4% in traditional pipelines to 8.7% in AI-optimized pipelines, while average deployment time decreased from 9.8 minutes to 6.4 minutes. Additionally, pipeline throughput increased from 46 builds per day to 68 builds per day. Among the evaluated algorithms, the Gradient Boosting model achieved the highest failure prediction accuracy of 93.5%.
The findings highlight the potential of artificial intelligence to enhance CI/CD pipeline reliability and efficiency through predictive analytics and intelligent automation. Integrating AI-driven optimization within DevOps pipelines can enable organizations to accelerate software delivery cycles, reduce operational failures, and improve overall system performance in modern software development environments.
