Automatic Container Specification Generation for Codebases

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The process of deploying a codebase to a containerized cloud platform is complex and time-consuming, especially for inexperienced users, due to the difficulty in selecting ideal frameworks and configurations, leading to delays and potential performance issues with suboptimal configurations.

Innovation Solution

A method for automatically generating a container specification file for a codebase based on extracted attribute names and values, using a combination of generative models and attribute extraction processes, which learns from public repositories to predict optimal container specification attributes and refine configurations, allowing user feedback for improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual configuration of container specifications is performed, then deployment precision and control are improved, but deployment time and complexity increase

Engineering Contradiction:

Improveconfiguration precisionVSAvoiddeployment time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating container specification files through machine learning models that analyze codebase attributes and predict optimal configurations without requiring manual user input for each configuration parameter

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-training machine learning models on extensive datasets of container specifications and codebases, enabling the models to make accurate predictions about optimal configurations before actual deployment is required

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If automatic generation of container specifications is implemented, then deployment time and ease of operation are improved, but configuration precision may deteriorate

Engineering Contradiction:

Improveease of deploymentVSAvoidconfiguration precision

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent replaces the mechanical system of manual configuration with an automated machine learning-based system that extracts codebase attributes, predicts optimal configurations, and generates container specification files automatically, significantly improving ease of operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where deployment outcomes and performance metrics are fed back into the machine learning models to continuously improve prediction accuracy and configuration precision over time

Inventive Principle:

Principle #23Feedback

3Reliability

If comprehensive attribute extraction and model training are performed, then configuration quality and reliability are improved, but system complexity and processing time increase

Engineering Contradiction:

Improveconfiguration reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex task of container configuration into distinct modules: codebase attribute extraction, attribute value prediction, container specification generation, and configuration refinement, allowing each component to be optimized independently while maintaining overall reliability

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11847431B2Automatic container specification file generation for a codebase

Publication Date: 2023.12.19 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.