ML Model for Software Deployment Risk Analysis
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Solution Overview
Problem
Current enterprise software is complex, with many components that can cause cascading issues during updates, making it difficult to predict and prevent potential problems in software deployments.
Innovation Solution
A system utilizing a machine learning model trained on historic trouble ticket data to analyze data related to planned software upgrades, generate a tag value, calculate a risk score, and determine client-specific actions to mitigate potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of software components and upgrades is performed, then detailed review of each component is possible, but the process becomes too time-consuming and cannot keep up with the volume of deployments and trouble tickets
Solution Approach 1:
The patent replaces manual mechanical analysis of software components with an automated machine learning system. The ML model automatically processes upgrade information, historical trouble ticket data, and component relationships to predict potential issues, eliminating the need for manual review while maintaining analysis accuracy.
Solution Approach 2:
The system performs self-service by automatically analyzing upgrade impacts without requiring manual intervention. The ML model autonomously processes data, identifies risks, and generates recommendations, allowing the system to serve itself rather than requiring continuous human analysis.
2Adaptability or versatility
If comprehensive analysis of all possible component combinations is performed, then complete coverage of deployment scenarios is achieved, but the complexity of support and trouble ticket resolution increases
Solution Approach 1:
The patent implements feedback by using historical trouble ticket data to train the ML model, which then provides predictions about potential upgrade issues. This feedback loop allows the system to learn from past problems and improve its ability to predict future issues, reducing support complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system performs preliminary action by analyzing upgrade impacts before they occur. The ML model evaluates potential issues in advance based on historical data and component relationships, allowing support teams to prepare for known issues rather than reacting to problems after they manifest, thus reducing operational complexity.
3Reliability
If proactive identification of potential issues is implemented, then service interruptions can be prevented, but the system complexity increases due to data processing and model training requirements
Solution Approach 1:
The patent applies preliminary action by using the ML model to identify potential upgrade issues before they cause service interruptions. The system analyzes upgrade information and historical data in advance, allowing proactive prevention of failures while maintaining manageable system complexity through automated processing.
Solution Approach 2:
The ML model serves as an intermediary between raw data and decision-making. It processes complex data about upgrades, components, and historical issues, transforming it into actionable predictions and recommendations. This intermediary layer manages system complexity by abstracting the complexity of data processing from the final decision-making process.
Data Source
AI summary
Systems, devices, methods, and computer readable media for determining potential issues with a software upgrade are disclosed. In one implementation, the disclosed system includes at least one processor and at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor. The operations include training a machine learning model on historic trouble ticket data; receiving data relating to a planned software upgrade; providing the received data to the trained machine learning model, wherein the trained machine learning model generates a tag value; calculating a risk score based on the tag value; and determining a client-specific action to be performed relating to the planned software upgrade and based on the risk score.