Expand description
Rule-Based Static Code Analysis for Model Debugging
This module identifies potential issues in neural-network training/inference source code, suggests optimizations, and flags common security anti-patterns.
Despite the type name AICodeAnalyzer (kept for API stability), this is a
deterministic, rule-based static analyzer: every finding comes from a
fixed substring/pattern check against the literal source text (see
AICodeAnalyzer::perform_deep_analysis and its sibling
detect_*/generate_* methods), not from any trained model or live
inference call. There is no network access, no model weights, and no
non-deterministic behavior – calling it twice on the same input always
produces the same result. Each rule carries a fixed confidence: f64
assigned when the rule was written, reflecting that rule’s own
specificity (how often that particular substring pattern is a true
positive in practice) – it is not a live-computed statistical
probability from any model, and must not be read as one.
Structs§
- AIAnalysis
Config - Configuration for AI code analysis
- AICode
Analyzer - Rule-based static code analysis engine for model debugging.
- Analysis
Metadata - Analysis
Performance Metrics - Code
Analysis Result - Comprehensive code analysis result
- Code
Location - Debugging
Assistance - Debugging
Step - Detected
Pattern - Documentation
Reference - Error
Context - Fusion
Opportunity - Identified
Issue - Memory
Optimization - Model
Context - Model context for analysis
- Optimization
Suggestion - Parallelization
Opportunity - Performance
Predictions - Probable
Cause - Redundant
Operation - Scaling
Characteristics - Security
Issue - Suggested
Fix - System
Info - Tensor
Operation - Tensor
Optimization Report