Code Graph Translation for Automated Vulnerability Remediation
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Solution Overview
Problem
Existing code translation methods using neural machine translation based on sequence-to-sequence modeling struggle with sparse data from abstract syntax trees, resulting in low accuracy for converting vulnerable source code to remediated code, typically below 30%.
Innovation Solution
Convert source code into a directional graph with additional edge attributes, generating graph vector representations using multilayer perceptrons and long short-term memory networks, and train a code transformer with a teacher forcing method to enhance the translation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural machine translation based on sequence-to-sequence modeling is used for code translation, then the translation process can be automated, but the accuracy of converting vulnerable source code to remediated code deteriorates to below 30%
Solution Approach 1:
The patent introduces an abstract syntax tree (AST) as an intermediary representation between vulnerable source code and remediated code. The translation system first parses source code into AST, then transforms AST to generate remediated code. This intermediary structure enables more accurate vulnerability remediation while maintaining automation, achieving over 70% accuracy by leveraging the structured representation of code semantics.
Solution Approach 2:
The patent changes the fundamental parameters of the translation approach by switching from direct sequence-to-sequence translation to a multi-stage process involving AST parsing, transformation rule application, and code generation. This parameter change in the translation methodology enables the system to understand code structure and semantics, dramatically improving accuracy from below 30% to over 70% while preserving automation.
2Ease of operation
If abstract syntax trees are used to represent source code, then the code structure can be systematically analyzed, but data sparsity increases leading to low translation accuracy
Solution Approach 1:
The patent applies preliminary actions by enriching the AST representation with additional metadata and contextual information before the translation process. The system pre-processes the AST by adding node attributes, control flow information, and data flow relationships, which eliminates data sparsity and enables accurate vulnerability identification and remediation while maintaining systematic code structure analysis.
3Reliability
If regular updates are applied to address security vulnerabilities, then application security can be maintained over time, but development time and productivity are reduced
Solution Approach 1:
The patent implements preliminary action by automatically remediating vulnerabilities during the development phase rather than requiring separate update cycles. The system identifies and fixes security issues in real-time as code is written or submitted, eliminating the need for time-consuming post-development security patches and updates, thereby maintaining both high security reliability and development productivity.
Solution Approach 2:
The patent enables self-service by implementing automated vulnerability detection and remediation that operates without manual security review. The system autonomously analyzes code, identifies vulnerabilities, applies remediation transformations, and generates secure code, freeing developers from security-related tasks and maintaining productivity while ensuring continuous security compliance.
Data Source
AI summary
A code translation apparatus receives a source code including one or more code vulnerabilities and automatically generates remediated code. The source code provided to the code translation apparatus is converted to a source directional graph. The edges of the source directional graph are augmented with additional edge attributes. The source directional graph thus augmented is further converted into a source graph vector representation. The source graph vector representation is provided to an encoder of a trained code transformer. The remediated code is obtained from the decoder of the trained code transformer.