AI-Generated CI/CD Pipelines for Faster, Error-Checked Deployment

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Solution Overview

Problem

Existing software development tools face challenges in efficiently creating and updating software deployment pipelines due to poor understanding and execution of CI/CD operations, leading to performance bottlenecks and misconfigurations, with integrated development environments lacking support for CI/CD tasks.

Innovation Solution

Employing generative AI models trained on CI/CD configuration files to predict and improve software deployment pipelines, providing interactive terminals for pipeline job execution, real-time feedback, and automatic completion of CI/CD configuration files, integrated with IDEs as plugins or extensions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual creation and updating of software deployment pipelines is performed, then flexibility and adaptability are maintained, but time consumption and labor effort increase significantly

Engineering Contradiction:

Improvepipeline generation efficiencyVSAvoidtime for creating and updating pipelines

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service by having the generative AI model automatically generate and update deployment pipelines based on project analysis, eliminating the need for manual pipeline creation while maintaining adaptability through AI-driven automation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of pipeline creation with an AI-based system that uses natural language processing and machine learning to automatically generate, interpret, and update deployment pipelines, significantly reducing time and effort

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual configuration of CI/CD pipelines is performed, then control over pipeline details is maintained, but error rates and misconfigurations increase

Engineering Contradiction:

Improvepipeline configuration accuracyVSAvoidmisconfigurations and errors

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system implements feedback mechanisms where the AI model continuously learns from pipeline execution results and configuration patterns, providing real-time validation and corrections to prevent misconfigurations while maintaining detailed control over pipeline settings

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual configuration with AI-driven automation that uses intelligent algorithms to generate and validate pipeline configurations, significantly reducing errors while maintaining comprehensive control through AI-based verification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If integrated development environments lack CI/CD support, then simplicity of IDE usage is maintained, but functionality and capability are limited

Engineering Contradiction:

ImproveCI/CD functionality integrationVSAvoidIDE functionality complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements universality by integrating multiple CI/CD functions including pipeline generation, configuration, execution, and monitoring into a single IDE platform, allowing developers to perform all DevOps tasks within one tool while maintaining simplicity through unified interface

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system provides self-service capabilities within the IDE where the generative AI model automatically handles CI/CD pipeline creation and management, reducing the complexity burden on users while expanding functionality through automated intelligence

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250356243A1Software deployment pipeline generation using generative artificial intelligence

Publication Date: 2025.11.20 DELL PROD LP

AI summary

Techniques are provided for software deployment pipeline generation using generative artificial intelligence (AI). One method comprises obtaining a trained generative AI model, trained using a plurality of continuous integration/continuous deployment (CI/CD) configuration files; obtaining information characterizing a selected software development project; and applying at least some of the information characterizing the selected software development project as one or more system prompts to the trained generative AI model, wherein the trained generative AI model predicts a portion of a software deployment pipeline associated with the selected software development project. The CI/CD configuration files used for training may be associated with an organization that is associated with the selected software development project. The predicted portion of the software deployment pipeline may comprise pipeline jobs, an automatic code completion and/or a correction of a syntax and/or a structure of at least one CI/CD configuration file being edited.