Adaptive Code Scanning With ML-Based Configuration Compliance

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Solution Overview

Problem

Existing code scanning technologies struggle to adapt to changing usage patterns of software components in codebases, leading to inefficiencies and potential non-compliance with approved configurations.

Innovation Solution

An adaptive code scanning system that utilizes machine learning to detect and adapt to unapproved uses of software components by comparing implemented parameters with approved configurations, autonomously learning and updating these configurations to align with actual usage patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If code scanning uses fixed approved configurations, then compliance is ensured, but adaptability to changing usage patterns deteriorates

Engineering Contradiction:

Improveadaptability to changing usage patternsVSAvoidcompliance with approved configurations

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The code scanning system transitions from static fixed configurations to dynamic adaptive configurations. The machine learning model continuously learns from actual usage patterns and automatically updates approved configurations, enabling the system to adapt to changing usage patterns while maintaining compliance. This dynamic adjustment mechanism resolves the contradiction by making the approval criteria flexible rather than rigid.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback loops where usage data from codebases is continuously fed back into the machine learning model. This feedback mechanism allows the system to learn from actual usage patterns and automatically refine its approval criteria. The feedback loop ensures that the system maintains compliance by learning from real-world usage while adapting to changing patterns, thus resolving the contradiction between compliance and adaptability.

Inventive Principle:

Principle #23Feedback

2Productivity

If manual review of code configurations is performed, then accuracy is improved, but productivity deteriorates

Engineering Contradiction:

Improvecode scanning efficiencyVSAvoidaccuracy of configuration approval

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The code scanning system performs self-service through automated machine learning-based configuration approval. The system autonomously learns from usage patterns and makes approval decisions without requiring manual review for each configuration. This self-service mechanism dramatically improves productivity while maintaining accuracy through the sophisticated machine learning models that continuously refine their judgment based on usage data.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical review processes with automated machine learning mechanisms. Instead of human reviewers manually checking each configuration, the system uses AI/ML models to automatically analyze and approve configurations based on learned patterns. This substitution improves productivity by eliminating manual labor while maintaining or improving accuracy through the intelligent algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of time

If code scanning is performed manually, then precision is maintained, but time consumption increases

Engineering Contradiction:

Improvetime for code scanningVSAvoiddetection accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system replaces manual code scanning with automated machine learning-based detection. The machine learning models continuously analyze codebases and detect configuration issues without human intervention. This automated mechanism reduces time consumption dramatically while maintaining or improving detection accuracy through the sophisticated algorithms that can process large amounts of code efficiently and with high precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12386614B2Adaptive code scanning

Publication Date: 2025.08.12 ORACLE INT CORP

AI summary

Systems, methods, and other embodiments associated with adaptive code scanning are described. In one example method, a valid configuration that specifies approved parameters of use for a software component is defined. Software code is scanned to detect that the software component exists in the software code. Where the component is detected, the software code is scanned to identify implemented parameters of use for the software component. The implemented parameters are compared to the approved parameters. Based on the comparison, the software component is determined to be not implemented according to the valid configuration. Where the software component is implemented according to the valid configuration, the method automatically determines to proceed with the automated action based on the implemented parameters. The automated action is performed to adapt to the implemented parameters.