Code Violation Prioritization with Machine Learning Scores
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Solution Overview
Problem
Existing methods for prioritizing code violations in software development fail to differentiate between minor and severe violations, leading to inefficient use of developer resources and potential security risks.
Innovation Solution
A system and method using machine learning to analyze code snippets, train a model to differentiate between vulnerable and non-vulnerable code, assign vulnerability probability scores, and rank code snippets based on severity for prioritized correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to prioritize code violations, then all violations are treated equally, but developer resources are wasted on minor issues while severe vulnerabilities remain unaddressed
Solution Approach 1:
The patent changes the parameter of violation assessment from binary (violation present/absent) to continuous (vulnerability probability score 0-1). This allows the system to differentiate between minor and severe violations by assigning different probability scores, enabling developers to prioritize fixing high-scoring violations that pose greater security risks while maintaining efficient resource allocation.
Solution Approach 2:
The patent replaces manual assessment of violation severity with an automated machine learning model. The model analyzes code snippets and automatically assigns vulnerability probability scores, substituting the mechanical process of human judgment with an automated system that consistently evaluates security risks and prioritizes violations based on learned patterns from training data.
2Measurement precision
If manual assessment of violation severity is performed, then expert judgment can differentiate between minor and severe violations, but the process is time-consuming and scales poorly
Solution Approach 1:
The patent creates a trained machine learning model that copies the decision-making patterns of expert assessors. By training the model on labeled data representing expert judgments, it replicates their ability to differentiate violation severity without requiring their direct involvement in each assessment, thus preserving measurement precision while eliminating time loss.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on comprehensive datasets before deployment. This preliminary action allows the model to learn expert assessment patterns in advance, so that during actual use, severity differentiation occurs automatically without requiring real-time expert intervention, thereby reducing assessment time while maintaining precision.
Data Source
AI summary
Method for prioritizing code violations in a computer program, using machine learning includes: analyzing the computer program for code violations; extracting code snippets containing violations from the computer program; training a machine learning model to differentiate between vulnerable and non-vulnerable code in the extracted code snippets; inputting the extracted code snippets to a trained machine learning model to assign a vulnerability probability score to each snippet, wherein each vulnerability probability score indicates a severity of the violation for a respective snippet; ranking the code snippets based on their respective vulnerability probability score, wherein a higher score indicates a higher likelihood of causing severe vulnerabilities; and displaying the ranked code snippets to be fixed for their code violations.