Code Refactoring TRIZ Cases
- Authorship Token Tracking for Human and AI Content Attribution
Automatic authorship tokens label human- and AI-written content during editing, improving provenance tracking, copyright compliance, and misattribution control.
- LLM Fine-Tuning Code Generation Without IP Function Exposure
IP functions are detected, flattened into native reference code, and tagged with unique IDs so LLM fine-tuning preserves code utility without exposing proprietary logic.
- Transformer Code Representations for Label-Free Code Smell Detection
Masked reserved-word prediction trains transformer code representations on unlabeled source code, reducing annotation effort while preserving code smell detection accuracy.
- Code Token Substitution for Long-Context ML Code Processing
Shorter substitute tokens let ML models handle longer code in one query, preserving context and improving translation or optimization speed.
- Application Modernization Sequencing Using Technology Stack Hierarchies
Historic stack relationships and user inputs are used to generate a real-time modernization sequence that cuts rework and compatibility issues.
- Code Paste Dependency Analysis for Context-Aware Optimization
Automatic dependency analysis adapts pasted code to the current development context, reducing manual edits and improving code quality.
- Integration Environment Migration Using Hub-and-Spoke Refactoring
AI-driven assessment, relationship mapping, and macro-service refactoring speed cloud migration while preserving business logic.
- Augmented Program Analysis for Legacy Code Translation Compatibility
Augmented metadata and code filtering guide LLM-based legacy code translation to improve compatibility, compilability, and memory handling.
- AI Code Enhancement Workflow With Trusted Validation Feedback
Multiple AI-generated code versions are sectioned, evaluated, and checked by trusted tools to improve efficiency without sacrificing reliability.