Automated Software Deployment Risk Scoring
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Solution Overview
Problem
Manual deployment decisions for software changes in enterprise systems often fail to consider all available data points, leading to potential service disruptions and downtime due to the premature deployment of unstable applications.
Innovation Solution
A system and method for automated deployment of software changes based on an application reliability analysis, which collects data from various resources, generates a change risk score using weighted averages of quality, performance, and stability parameters, and determines whether to deploy changes based on this score, thereby preventing disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated deployment is implemented, then deployment reliability is improved, but system complexity increases
Solution Approach 1:
The system automatically assesses application readiness and makes deployment decisions without human intervention. The automated assessment engine collects data from multiple sources, calculates risk scores, and determines deployment eligibility, allowing the system to serve itself rather than requiring manual analysis and decision-making.
Solution Approach 2:
The patent replaces manual human assessment with an automated computational system. Instead of relying on human analysts to evaluate deployment readiness, the system uses algorithms that process quantitative data, calculate risk scores, and automatically determine deployment decisions, substituting mechanical human judgment with automated computational logic.
2Device complexity
If manual deployment decisions are used, then system complexity is reduced, but measurement precision of deployment readiness deteriorates
Solution Approach 1:
The system replaces subjective human judgment with objective computational assessment. The automated engine processes quantitative metrics from multiple data sources, applies consistent calculation rules, and generates precise risk scores that objectively measure deployment readiness without the variability inherent in manual assessment.
Solution Approach 2:
The system continuously collects data from applications, infrastructure, and deployment history, then uses this feedback to calculate risk scores and adjust deployment decisions. This closed-loop feedback mechanism ensures that assessment precision is maintained through ongoing data collection and analysis, allowing the system to learn from past deployments and improve future assessments.
3Measurement precision
If comprehensive data collection is implemented, then measurement precision of application reliability is improved, but loss of time increases
Solution Approach 1:
The system performs data collection and readiness assessment in advance of actual deployment. By continuously monitoring application metrics, infrastructure status, and deployment history, the system prepares comprehensive reliability data beforehand, so that when deployment decisions are needed, the analysis is already complete and can be executed quickly without time loss.
Solution Approach 2:
The automated assessment engine operates continuously, constantly collecting and analyzing data from multiple sources. This continuous operation ensures that reliability metrics are always up-to-date and ready for deployment decisions, eliminating the need for periodic batch processing and reducing overall time loss by maintaining constant readiness assessment.
Data Source
AI summary
A system and method for controlling deployment of one or more changes to one or more applications of a computer system based upon an application reliability analysis. The system collects information related to one or more applications deployed on a system which require changes from one or more resources; generates a change risk score for each application based at least in part on a weighted average of one or more parameters affecting system quality, a weighted average of one or more parameters affecting system performance, and a weighted average of one or more parameters affecting system stability, and determines whether to perform an automated deployment process or prevent the deployment of application changes for each application.