Dynamic DevOps Pipeline Generation for Change-Specific Builds
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Solution Overview
Problem
Existing DevOps systems take the same amount of time for each build and release regardless of the type of source code file and change type, and cannot predict whether the change will successfully achieve the intended target.
Innovation Solution
A dynamic DevOps pipeline generation system that uses AI/ML and quantum computing to analyze source code changes, create a knowledge graph, and employ quantum annealing to optimize the pipeline for relevant changes, generating a plan for efficient build and release.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static DevOps pipeline is used for all builds, then the system is simple to manage, but the build time is excessive and not optimized for different change types
Solution Approach 1:
The patent implements dynamic pipeline generation where the DevOps pipeline is automatically adapted based on the type of changes detected in the source code. The system analyzes change types (e.g., bug fixes, new features, configuration changes) and dynamically configures the pipeline steps, tools, and parameters accordingly, transforming the static pipeline into a dynamic one that optimizes build time for each specific scenario.
Solution Approach 2:
The system changes multiple pipeline parameters dynamically including build configuration, test selection, deployment targets, and resource allocation based on the analyzed change type. By modifying these parameters according to the specific build context, the system achieves optimized build times without requiring completely different pipelines for each scenario.
2Loss of time
If the DevOps system processes all source code files uniformly, then the processing logic is simple, but the time consumption increases unnecessarily
Solution Approach 1:
The patent segments the source code base into different change categories (e.g., critical files, minor modifications, configuration changes) and applies different processing strategies to each segment. The system identifies which files have changed and categorizes them, then selectively applies build, test, and deployment operations only to the relevant segments, avoiding uniform processing of all files.
Solution Approach 2:
The system applies different processing quality and depth to different parts of the codebase based on their importance and change type. Critical files receive more thorough testing and validation, while minor changes receive streamlined processing. This local quality approach reduces overall time consumption by avoiding unnecessary deep processing of low-risk changes.
3Reliability
If the DevOps system uses a fixed pipeline for all builds, then the system is easy to implement, but it cannot predict whether the change will achieve the intended target
Solution Approach 1:
The patent implements preliminary analysis of the source code changes before the actual build process. The system pre-evaluates the change type, identifies potential risks, determines the appropriate pipeline configuration, and even predicts the likelihood of success. This preliminary action allows the system to prepare the optimal pipeline in advance, improving the probability of achieving the intended target.
Solution Approach 2:
The system incorporates feedback mechanisms that analyze build results, test outcomes, and deployment status to continuously improve pipeline generation. By learning from past builds and their outcomes, the system refines its predictions and optimizations, thereby improving reliability over time while maintaining manageable complexity through iterative learning.
Data Source
AI summary
Apparatus for a dynamic development operations (“DevOps”) pipeline generation may include the current build and release of the source code files and related changed units within a data repository. The data repository may include a plurality of data associated with the current build and release of the source code file. A software system may collect the data from the repository. The collected data may be input into an artificial intelligence or machine learning (“AI/ML”) module. The AI/ML module may use a language learning module (“LLM”) to create a plurality of nodes from the data. The LLM may create a knowledge graph from the nodes. The knowledge graph may be input into a quantum computing system to create attention matrices. The attention matrices may be input into a quantum annealing system to determine the DevOps plan. A transformer neural network (“TNN”) may output and execute the DevOps plan.