Optimizing continuous integration and continuous deployment pipelines with machine learning: Enhancing performance and predicting failures
D R, Juby Mathew
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.