Structured Change Records for Computing Environment Update Risk
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Solution Overview
Problem
Existing methods for determining the risk of software and hardware updates in computing environments are complex and specific to incremental software updates, requiring extensive training and are unsuitable for hardware changes, leading to potential malfunctions and downtime.
Innovation Solution
A computer-implemented method using a trained predictive model to analyze change records with machine learning, calculating a risk score based on historical data to flag potential failures or malfunctions in computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning methods are used to determine risk of software updates, then risk assessment capability is improved, but device complexity and training requirements increase significantly
Solution Approach 1:
The patent transforms the risk assessment approach by changing the input parameters from complex code difference analysis to structured change record fields. The predictive model receives simplified parameters including change category, number of configuration items impacted, number of services impacted, and other metadata fields, rather than processing entire codebases or performing extensive code comparisons.
Solution Approach 2:
The patent extracts only the essential risk-related features from change requests and stores them in structured change records. Instead of analyzing entire software updates or code differences, the system extracts key parameters such as change category, scope of impact, and historical performance data, feeding only these extracted features into the predictive model.
2Reliability
If existing machine learning methods are used for software update risk assessment, then risk prediction capability is improved, but adaptability to hardware changes deteriorates
Solution Approach 1:
The patent creates a universal risk assessment system that handles both software and hardware changes through the same predictive model. The change record structure and model inputs are designed to be domain-agnostic, accepting change categories and impact parameters whether the change involves software updates, hardware modifications, or infrastructure changes, making the system broadly applicable across different change types.
3Measurement precision
If comprehensive change analysis is performed, then measurement precision of risk is improved, but loss of time for processing increases
Solution Approach 1:
The patent performs preliminary action by pre-structuring change information into standardized change records with predefined fields before risk assessment. Change metadata such as category, scope, and impact areas are captured and organized in advance during change request creation, so that when risk assessment is needed, the predictive model receives pre-processed, ready-to-analyze data without requiring time-consuming extraction or analysis of raw change information.
Data Source
AI summary
A computer-implemented method for flagging risk when handling updates to a computing environment is disclosed. The method includes receiving a change request comprising one or more change features associated with a change in hardware and/or software within the computing environment, and receiving a change record associated with the change request, the change record comprising one or more fields corresponding to the one or more change features. The method further includes calculating a risk score for the change by applying a trained predictive model to the one or more fields, the predictive model having been trained on a dataset comprising historical changes to the computing environment and their associated historical change records. If the calculated risk score is above a predetermined threshold, a flag is raised to the change request to notify a user.