Software Application Status Assessment for Vulnerability-Aware Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the ability to assess the effects of modifications to software applications, which can jeopardize functionality or security, without understanding the context and potential risks involved.
Innovation Solution
A status assessment system that evaluates the status of a software application before and after modifications, using machine learning to predict vulnerabilities and compare changes to context-specific thresholds, initiating remedial measures if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software applications are modified with new components to improve functionality, then adaptability is improved, but security and system stability deteriorate due to unknown vulnerabilities and risks
Solution Approach 1:
The system performs preliminary assessment of status changes before allowing modifications to be deployed. It evaluates the difference between the original status and the modified status, and only permits changes that satisfy predefined thresholds, thereby preventing security vulnerabilities before they can harm the system
Solution Approach 2:
The system continuously monitors software applications and provides feedback about status changes. When a modification causes the status difference to exceed the threshold, the system sends notifications and can automatically roll back changes, creating a closed-loop control mechanism that maintains security while allowing safe adaptations
2Adaptability or versatility
If modular components are added or removed to improve scalability, then adaptability is improved, but system stability deteriorates due to potential negative impacts on overall application health
Solution Approach 1:
Before deploying modular component changes, the system assesses the predicted status change using machine learning models. It calculates the difference between original and modified status, and only allows changes that satisfy the stability threshold, preventing destabilizing modifications before they occur
Solution Approach 2:
The system monitors the health status of software applications continuously and provides feedback when modifications cause excessive status changes. When instability is detected, the system can notify operators or automatically roll back changes, maintaining system stability while permitting safe modular adaptations
3Productivity
If modifications are made without assessment to improve productivity, then ease of operation is improved, but reliability deteriorates due to unknown consequences and risks
Solution Approach 1:
The system performs rapid preliminary assessment of modifications using machine learning models that predict status changes based on historical data. This allows quick evaluation of modification risks, enabling fast approval of safe changes while blocking harmful ones, thus maintaining both productivity and reliability
Solution Approach 2:
The system provides immediate feedback about modification safety to operators and automated deployment systems. When a modification is predicted to cause excessive status change, the system notifies operators and can automatically prevent deployment, allowing rapid identification and correction of risky changes without halting the entire development process
Data Source
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.