Optimizing continuous integration and continuous deployment pipelines with machine learning: Enhancing performance and predicting failures

Abstract

Continuous integration and continuous deployment (CI/CD) pipelines form the backbone of modern software development but typically suffer from long build times, repeated failures, and inefficient use of resources. This work presents a machine learning-based framework that systematically improves pipeline performance through predictive modelling. More specifically, the work will focus on developing a Support Vector Machine model to predict pipeline failures; it minimizes build times through optimized resource allocation while building dynamic frameworks for continuous improvement of CI/CD pipelines. The study assumes an exhaustive literature review and propounds a new approach by using an SVM model. Critical performance metrics such as the build duration, test pass/ fail rates, and resource consumption are analysed and the framework is found to have significant improvements by the measurements: a 33% decrease in the build time, a 60% decrease in the failure rates, and optimization of CPU and memory utilization. The experiments validated the outcome of being scalable in an intelligent manner such that persistent problems with CI/CD are solved in modern DevOps practices. This work provided initial groundwork by bringing in the concept of ML in CI/CD process, aiming to enhance reliability and efficiency in the pipelines that would lead towards major strides in adaptive systems in the context of software engineering workflows.