AI Change Risk Assessment With Incident Feedback Learning
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Solution Overview
Problem
Software companies face challenges in effectively managing change requests in production to avoid system outages and incidents, with current methods relying on subjective rules and static surveys, leading to increased risk and downtime.
Innovation Solution
A machine-learning based model that interacts with change and incident owners to assess change risk, gather responses, and update its association between change features and incident features, providing intelligent alerts and predictions to mitigate incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static surveys and subjective rules are used to assess change risk, then the assessment process is simple and quick, but the accuracy and reliability of risk assessment deteriorates
Solution Approach 1:
The patent replaces manual, rule-based risk assessment methods with an automated machine learning model that objectively analyzes change requests. The model uses historical incident data and change features to predict risk, substituting human subjectivity with algorithmic analysis while maintaining fast assessment throughput.
Solution Approach 2:
The system implements feedback loops where incident outcomes are fed back into the machine learning model to continuously improve risk prediction accuracy. The model learns from historical data and adjusts its predictions based on actual incident patterns, enabling both speed and accuracy.
2Ease of operation
If manual change management processes are used, then implementation is straightforward, but time loss for determining cause of performance changes increases
Solution Approach 1:
The system performs preliminary risk assessment before changes are deployed by analyzing change requests against historical data and incident patterns. This advance preparation identifies potential issues before they occur, reducing the need for lengthy post-incident investigations while maintaining operational simplicity.
3Stability of the object's composition
If rules-based change management is applied, then consistency is maintained, but adaptability to complex modern IT architectures deteriorates
Solution Approach 1:
The patent transitions from static rules to a dynamic machine learning model that adapts to complex IT architectures. The model continuously learns from new data and adjusts its risk predictions based on changing system conditions, maintaining consistency through algorithmic logic while adapting to architectural complexity.
4Measurement precision
If comprehensive change analysis is performed, then risk identification improves, but system complexity and resource requirements increase
Solution Approach 1:
The system extracts only the most relevant features from change requests and historical incident data to feed into the machine learning model. This selective extraction maintains high risk identification accuracy while avoiding the complexity of analyzing every possible parameter, reducing system resource requirements.
Data Source
AI summary
A method for improving the quality of a machine-learning based model includes generating a first query requesting a description of a change proposed to a system and an intended outcome of the change proposed; receiving a first response; generating a second query providing a risk of an incident associated with the change proposed and requesting justification of the change proposed in view of the risk; receiving a second response; generating a third query requesting an implementation plan for the change proposed; receiving a third response; generating an alert to an incident owner providing the description, intended outcome, risk, justification, and implementation plan of the change proposed; receiving a risk confirmation or rejection from the incident owner confirming or rejecting a relationship between the change proposed and the risk; and updating the machine-learning based model to learn an association between extracted features of the change and extracted features of the incident.