ML Classification of Code Changes for Vulnerability Triage
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Solution Overview
Problem
Existing source code management systems face challenges in accurately identifying cybersecurity vulnerabilities, leading to false positive detections that consume resources and delay the resolution of actual vulnerabilities, thereby increasing the risk of malware infections.
Innovation Solution
A source code management system utilizing machine learning and curated features for classifying source code changes into vulnerability-inducing, vulnerability-fixing, or likely normal categories, enabling early detection and targeted resource allocation for actual vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional source code vulnerability detection methods are used, then all code changes are flagged for review, but this results in high false positive rates that consume resources and delay resolution
Solution Approach 1:
The system performs preliminary classification of code changes using machine learning models before full vulnerability analysis. By pre-processing commits with trained classifiers that evaluate features like code patterns, developer history, and change context, the system filters out low-risk changes early, allowing resources to focus on high-risk commits and reducing false positives while maintaining detection accuracy
Solution Approach 2:
The system applies different analysis depths and resource allocations to different code changes based on their risk classification. High-risk commits receive comprehensive manual and automated review, while low-risk commits undergo lighter scrutiny. This localized quality approach ensures thorough investigation of actual vulnerabilities while avoiding wasteful review of benign changes, thereby improving both detection accuracy and review efficiency
2Reliability
If comprehensive security review is performed on all snapshots, then actual vulnerabilities are detected, but resources are wasted on false positives
Solution Approach 1:
The system performs partial security review on code changes based on risk assessment. Instead of applying full comprehensive review to all snapshots, the machine learning model evaluates multiple features (code complexity, developer track record, change patterns) to identify only high-risk commits warranting full review. This partial action approach maintains reliable vulnerability detection for actual threats while significantly reducing processing resource consumption by excluding low-risk commits from intensive analysis
3Measurement precision
If false positive detections are investigated thoroughly, then detection accuracy is maintained, but time to resolve actual vulnerabilities increases
Solution Approach 1:
The system performs preliminary risk assessment and classification before full vulnerability investigation. Machine learning models pre-evaluate commits using features such as code change patterns, developer history, and contextual information to identify high-probability vulnerability cases. This preliminary action ensures that thorough investigations are directed only at commits with actual vulnerability potential, maintaining detection precision while minimizing time loss by avoiding detailed analysis of false positives
Solution Approach 2:
The system incorporates feedback loops where resolution outcomes of classified commits are fed back into the machine learning training process. When commits initially classified as high-risk are determined to be false positives, or when low-risk commits reveal actual vulnerabilities, this feedback refines the model's future classifications. This continuous learning improves detection precision over time while reducing unnecessary investigations, thereby decreasing vulnerability resolution time
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing computer software source code. One of the methods includes receiving an updated snapshot of a source code file that comprises a change to an existing snapshot of the source code file maintained at a code repository for a software project; obtaining feature data; processing the feature data using a machine learning model to generate a classification output that classifies the updated snapshot into one of a plurality of categories; and performing, based on the classification output, an action with respect to the updated snapshot.