Cognitive Release Evaluation for Software Deployment Safety
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Solution Overview
Problem
Conventional software deployment systems lack direct integration between production deployment tools and release management tools, leading to manual intervention and potential human errors, which can result in negative impacts on system environments due to inadequate assessment of software release change requests.
Innovation Solution
A system utilizing artificial intelligence and machine learning to evaluate software release change requests through a cognitive release evaluation module and smart decision engine, generating confidence scores and authentication tokens to ensure changes meet threshold limits before deployment, thereby minimizing manual intervention and preventing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used in software deployment, then flexibility and human judgment are improved, but human errors and negative impacts on system environment increase
Solution Approach 1:
The patent introduces an intermediary system (deployment bot with machine learning models) that mediates between human operators and the deployment environment. This intermediary automatically assesses deployment requests, identifies potential failure points, and provides recommendations, thereby reducing human errors while preserving the benefits of human judgment and flexibility in the deployment process.
2Productivity
If direct integration between deployment tools and release management tools is implemented, then manual intervention is reduced, but system complexity increases
Solution Approach 1:
The deployment bot serves as an intermediary layer that integrates deployment tools and release management tools without requiring complex direct integration. The bot receives deployment requests, automatically assesses them using machine learning models, and coordinates with relevant systems, thereby improving productivity while managing system complexity through a modular intermediate component.
Solution Approach 2:
The system segments the deployment process into distinct automated assessment phases and human approval phases. The machine learning models independently evaluate deployment requests for potential failure points, separating the complex analytical work from the final decision-making process, which simplifies the overall system architecture while improving efficiency.
3Reliability
If automated assessment of deployment requests is implemented, then human errors are reduced, but processing time and computational resources increase
Solution Approach 1:
The machine learning models are trained in advance on historical deployment data to learn patterns of successful and failed deployments. This preliminary training enables the models to quickly assess new deployment requests without requiring extensive real-time computation, thereby reducing assessment processing time while maintaining high deployment accuracy.
Solution Approach 2:
The system performs partial automated assessment by focusing machine learning analysis on identifying specific potential failure points rather than evaluating every aspect of a deployment request. This selective approach reduces computational overhead and processing time while still achieving reliable deployment accuracy for critical failure modes.
Data Source
AI summary
Systems, computer program products, and methods are described herein for evaluating, validating, and implementing software release change requests to a system environment based on artificial intelligence input. The present invention may be configured to receive a software release change request including a change to be made to a configuration item of a system environment, determine, based on a change inference database, potential failure points associated with deploying the software release change request in the system environment, and determine, based on the potential failure points, a confidence score for the change using a cognitive release evaluation system module comprising an artificial intelligent or machine learning engine.