ML Risk Awareness for Code Deployment Incident Prevention
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Solution Overview
Problem
Software companies face challenges in managing code deployment risks due to unclear vulnerability assessments, leading to system outages, increased costs, and adverse customer impact, with existing methods being rule-based and manually subjective.
Innovation Solution
A machine-learning based model is trained to analyze metadata from previous modifications and incidents, determining a risk level for proposed changes by learning associations between code modifications and system incidents, providing real-time alerts and suggestions for reducing risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based manual methods are used to assess code deployment risks, then the process is simple to implement, but the measurement precision and reliability of risk assessment deteriorates
Solution Approach 1:
The patent replaces manual rule-based risk assessment with an automated machine learning system that processes code metadata and incident data. The ML model automatically learns patterns and associations between code changes and system incidents, eliminating subjective human judgment and improving measurement precision while accepting increased system complexity through automated processing pipelines.
Solution Approach 2:
The patent introduces metadata extraction and feature engineering as intermediary steps between code modifications and risk assessment. These intermediaries transform raw code data into structured features that the ML model can process, enabling accurate risk prediction without requiring the model to directly analyze complex code structures.
2Reliability
If automated machine learning models are used to determine risk levels, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by collecting and storing metadata from code repositories, build systems, and incident tracking systems before risk assessment is needed. This pre-processing and storage of historical data enables the ML model to quickly evaluate risks without performing complex analysis in real-time, reducing operational complexity while maintaining reliability.
Solution Approach 2:
The ML model automatically determines risk levels without requiring manual configuration or intervention. The system self-trains on historical data, automatically updates risk assessments for proposed changes, and provides recommendations without human input, improving reliability through consistent automated evaluation while managing complexity through self-service operations.
3Productivity
If manual subjective rules are applied to prevent outages, then the ease of operation is maintained, but the productivity and incident resolution speed deteriorates
Solution Approach 1:
The patent implements feedback loops where the ML model continuously learns from incident data and outcomes. The system monitors actual incidents, compares them with predicted risks, and uses this feedback to improve future risk assessments. This automated feedback mechanism increases productivity by rapidly resolving incidents based on learned patterns while managing operational complexity through systematic data collection and model updating.
Solution Approach 2:
The patent replaces manual subjective rule application with automated ML-based risk determination. The system automatically analyzes proposed code changes, retrieves relevant historical data, and determines risk levels without human intervention, significantly improving incident resolution speed while accepting reduced operational simplicity due to automated system management requirements.
4Measurement precision
If comprehensive metadata analysis is performed to improve risk assessment accuracy, then measurement precision improves, but loss of time in processing increases
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive metadata, rather than analyzing all available data. The ML model identifies and processes key characteristics of code changes and historical incidents that are most predictive of risk, eliminating unnecessary processing of irrelevant information. This selective extraction maintains measurement precision while significantly reducing processing time and computational resources required.
Data Source
AI summary
A method for training and using a machine-learning based model to reduce and troubleshoot incidents in a system may include receiving first metadata regarding a previous modification, extracting a first feature from the received first metadata, receiving second metadata regarding a previous incident, extracting a second feature from the received second metadata, training the machine-learning based model to learn an association between the previous modification and the previous incident, based on the extracted first feature and the extracted second feature, and using the machine-learning based model to determine a risk level for a proposed modification to a system.