AI-ENHANCED CONTINUOUS INTEGRATION AND DEPLOYMENT (CI/CD) PIPELINE FOR SCALABLE CLOUD-NATIVE APPLICATIONS

Abstract

The present invention relates to an AI-enhanced Continuous Integration and Deployment (CI/CD) pipeline designed for scalable cloud-native applications. The pipeline leverages artificial intelligence (AI) and machine learning (ML) techniques to optimize key stages of the software development lifecycle, including code analysis, test optimization, resource allocation, failure prediction, and continuous learning. By automating code quality assessments, intelligently selecting relevant tests, dynamically allocating cloud resources, and predicting potential deployment failures, the system improves the efficiency, scalability, and reliability of the CI/CD process. The invention provides a robust solution for optimizing cloud-native application deployment, reducing costs, minimizing downtime, and ensuring faster, more reliable software delivery.

Specification

Description:The embodiments of the present invention generally relate to the field of software engineering, specifically to the enhancement of Continuous Integration and Continuous Deployment (CI/CD) pipelines for scalable cloud-native applications. More particularly, the invention leverages artificial intelligence (AI) and machine learning (ML) techniques to optimize various stages of the CI/CD process, such as code analysis, test selection, resource allocation, failure prediction, and pipeline performance, enabling faster, more efficient, and reliable software development and deployment in cloud environments.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disc , Claims:1. A method for enhancing continuous integration and deployment (CI/CD) pipelines for scalable cloud-native applications, comprising:
• Analyzing code commits using an artificial intelligence model to predict integration issues and assess code quality;
• Selecting relevant test cases using machine learning to optimize the testing process based on code changes;
• Dynamically allocating cloud resources using AI-based predictions for efficient scaling of the cloud-native application;
• Detecting deployment anomalies and predicting potential failures using machine learning algorithms;
• Continuously learning from past pipeline data to improve the overall pipeline performance.

2. The method of claim 1, wherein the code analysis uses natural language processing (NLP) and static code analysis to assess code quality.

3. The method of claim 1, wherein the test optimization module selects test cases based on historical test execution data and identifies the most relevant tests for the current code changes.

4. The me

Documents