Software Application Modification Assessment With Vulnerability Prediction

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Solution Overview

Problem

Existing systems lack the ability to assess the effects of modifications to software applications with modular components, which can jeopardize functionality or security without proper contextual understanding.

Innovation Solution

A status assessment system that evaluates the impact of modifications by comparing pre- and post-modification statuses using machine learning models to predict vulnerabilities and resilience, applying adaptive thresholds based on the application's context to ensure safe operation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If modular components are added or modified to software applications, then adaptability and scalability are improved, but security vulnerabilities and system stability deteriorate

Engineering Contradiction:

Improvemodular modification capabilityVSAvoidsystem stability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary assessment of status changes before allowing modifications to be deployed. It calculates predicted status changes using historical data and machine learning models, and compares these predictions against thresholds to prevent modifications that would cause excessive status changes, thereby maintaining system stability while allowing safe modular modifications.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual status changes after modifications are deployed and uses this feedback to refine its machine learning models. By comparing predicted status changes with actual outcomes, the system improves its ability to accurately assess future modifications, creating a closed-loop feedback mechanism that enhances both security and adaptability over time.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If modular components are added or modified to software applications, then functionality and scalability are improved, but security vulnerabilities increase

Engineering Contradiction:

Improvecomponent modification capabilityVSAvoidsecurity vulnerabilities

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system takes preliminary anti-action by blocking modifications that are predicted to cause excessive status changes before they can be deployed. By using machine learning models to predict vulnerabilities and comparing against thresholds, the system prevents potentially harmful modifications from reaching production, thereby counteracting security risks before they materialize.

Inventive Principle:

Principle #9Preliminary anti-action

3Reliability

If strict assessment thresholds are applied to all software applications, then security and stability are improved, but flexibility and deployment speed deteriorate

Engineering Contradiction:

Improveapplication securityVSAvoidmodification deployment speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system applies different assessment thresholds to different applications based on their criticality and historical behavior. Critical applications receive stricter assessment with lower thresholds, while non-critical applications receive more lenient assessment with higher thresholds. This localized quality approach ensures security for critical systems while maintaining deployment speed for less critical systems.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts assessment thresholds based on application characteristics, historical status changes, and criticality levels. Rather than applying static thresholds to all applications, the system adapts thresholds in real-time, making the assessment process flexible and responsive to specific application needs, thereby balancing security requirements with deployment efficiency.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12443725B2Assessing status changes caused by modifications to software applications

Publication Date: 2025.10.14 CAPITAL ONE SERVICES LLC

AI summary

Methods and systems are described herein for assessing status changes caused by modifications to software applications. The system may identify a status associated with an application. For example, the status may be based on a first length of time that the application is inactive based on a current number of vulnerabilities. The system may detect a modification of a component of the application and may determine, using a machine learning model, a predicted number of vulnerabilities introduced into the application by the modification. The system may identify an updated status associated with the application based on the predicted number of vulnerabilities. The system may determine a difference between the status and the updated status and based on the difference, may cause the modification to cease.