AI Code Update Prediction for Deployment Failure Prevention

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Solution Overview

Problem

The exponential increase in computer code updates has made it difficult to maintain and predict potential issues caused by these updates, necessitating a mechanism to determine whether updates are likely to cause failures in other parts of the code.

Innovation Solution

An artificial intelligence model is developed to analyze computer code updates using machine learning, identifying code segments, determining differences, and predicting potential issues by training on time series data from previous updates, generating action identifiers to remediate any problems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If computer code is updated frequently to meet growing functional requirements, then productivity and adaptability improve, but the risk of introducing failures and maintenance difficulty increase

Engineering Contradiction:

Improvecode update frequencyVSAvoidcode stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary analysis of code updates before deployment by training an AI model on historical code change data. The model predicts potential failures and compatibility issues in advance, allowing developers to address problems before they affect production systems, thus maintaining both high update frequency and code reliability

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If code is divided into multiple segments for easier maintenance, then ease of operation improves, but device complexity increases

Engineering Contradiction:

Improvecode maintainabilityVSAvoidcode structure complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system automatically segments code into functional modules and uses AI to analyze relationships between segments. This enables targeted updates to specific segments without affecting others, improving maintainability while the AI model manages the complexity of inter-segment relationships to prevent overall system complexity from increasing

Inventive Principle:

Principle #1Segmentation

3Reliability

If manual code review processes are used to ensure quality, then reliability improves, but productivity decreases

Engineering Contradiction:

Improvecode qualityVSAvoidupdate speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system replaces manual code review processes with an AI-based automated review system. The machine learning model analyzes code changes, predicts failures, and generates remediation recommendations automatically, maintaining high code quality standards while eliminating the time constraints of manual review and significantly increasing update speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260017172A1Predicting computer code update conditions using artificial intelligence

Publication Date: 2026.01.15 CAPITAL ONE SERVICES LLC

AI summary

Methods and systems are described herein for building and executing an artificial intelligence model that predicts whether a computer code update is likely to cause an issue. In particular, the system may receive a potential code update and identify update parameters associated with the potential code update. The system may then input the potential code update and the update parameters into a machine learning model to receive a prediction about the potential code update to be displayed to a user.