Graph Attention Network for Software Code Analysis
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Solution Overview
Problem
Existing methods for analyzing and optimizing computer software code are limited by their reliance on deterministic algorithms, require source code availability, and struggle with structural features like function calls and branching, making them ineffective for detecting malicious code and enhancing runtime performance.
Innovation Solution
A computing system utilizing a graph attention neural network that generates dependence graphs and embedding vectors from machine code, allowing for automatic analysis and enhancement of computer program code, including malicious code detection and performance optimization through loop vectorization and optimization pass ordering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deterministic algorithms are used for code analysis, then the approach is simple and interpretable, but it is limited by the number, variety and effectiveness of heuristics that can be applied
Solution Approach 1:
The patent replaces deterministic mechanical algorithms with a neural network-based learning system. The neural network automatically learns code analysis patterns from training data, substituting the need for manually crafted heuristics with adaptive neural network models that can generalize to unseen code patterns while maintaining interpretability through attention mechanisms.
Solution Approach 2:
The patent changes the fundamental parameter of code analysis from fixed deterministic rules to learnable probabilistic patterns. By training neural networks on large code corpora, the system adapts its parameters (weights and biases) to capture diverse code patterns, enabling versatility without requiring explicit programming of each pattern.
2Ease of operation
If source code is used for analysis, then semantic understanding is easier, but source code availability is required which is not always the case
Solution Approach 1:
The patent creates a dynamic system that adapts to different input formats. The neural network is trained to handle both source code and compiled binary code, automatically adjusting its analysis approach based on the input type. This dynamic capability allows the system to operate reliably whether source code is available or only compiled artifacts exist.
Solution Approach 2:
The patent develops a universal code analysis system that performs the same security and optimization analysis functions regardless of whether the input is source code, assembly code, or machine code. The neural network architecture is designed to extract meaningful features from multiple code representations, making the system reliable across different code availability scenarios.
3Measurement precision
If natural language processing methods are used, then language understanding is improved, but structural features such as function calls, branching, and the interchangeable order of statements cannot be properly analyzed
Solution Approach 1:
The patent segments the code into structured graphical representations that preserve hierarchical relationships. The neural network processes code as a graph of statements, function calls, and control flow edges, segmenting the analysis into manageable components while maintaining the structural relationships necessary for accurate detection of function calls, branching, and statement ordering.
Solution Approach 2:
The patent transitions from linear sequence analysis to multi-dimensional graph-based analysis. By representing code as a graph structure with nodes and edges capturing different relationships (control flow, data flow, call hierarchy), the system adds dimensional information that enables precise structural feature detection while the neural network learns to navigate this complex representation efficiently.
4Productivity
If automated analysis is implemented, then productivity is improved, but the ability to detect malicious code and optimize runtime performance requires advanced learning approaches
Solution Approach 1:
The patent implements feedback loops where the neural network's predictions are evaluated against ground truth labels during training, and where attention mechanisms provide interpretability feedback to validate detection accuracy. This feedback mechanism enables the automated system to continuously improve its malicious code detection effectiveness while maintaining high productivity through batch processing capabilities.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that generates a dependence graph based on a plurality of intermediate representation (IR) code instructions associated with a compiled program code, generates a set of graph embedding vectors based on the plurality of IR code instructions, and determines, via a neural network, one of an analysis of the compiled program code or an enhancement of the program code based on the dependence graph and the set of graph embedding vectors. The technology may provide a graph attention neural network that includes a recurrent block and at least one task-specific neural network layer, the recurrent block including a graph attention layer and a transition function. The technology may also apply dynamic per-position recurrence-halting to determine a number of recurring steps for each position in the recurrent block based on adaptive computation time.