Distributed Code Search Using Explainable AI Embeddings
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Solution Overview
Problem
The complexity of modern computer networks, lack of centralized platforms, and varying coding standards make it difficult to locate and retrieve precise code segments across distributed environments, and using code samples as search inputs introduces opacity in the search process, reducing user control and understanding.
Innovation Solution
A dual-layered approach using AI models to compare code sample characteristics and generate human-readable descriptions, aligning feature-based comparisons with pre-labeled characteristics to provide structured and interpretable search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If code samples are used as search inputs to find similar code, then search accuracy and relevance are improved, but user control and understanding of the search process are reduced
Solution Approach 1:
The patent introduces an intermediary system that includes a code embedding model and a dual-layer retrieval mechanism. This intermediary translates code samples into embeddings and manages the search process, balancing automated accuracy with user control through configurable parameters and explainable results.
Solution Approach 2:
The system implements feedback mechanisms where search results are presented with explanations of similarity metrics, allowing users to understand why certain code samples are returned. Users can refine searches based on this feedback, maintaining control while benefiting from automated code similarity analysis.
2Measurement precision
If AI models are used to analyze code structure and syntax, then search accuracy is improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct modular components: a code embedding model for feature extraction, a dual-layer retrieval system for searching, and a result explanation generator. Each module performs a specific function, making the overall complex system manageable and maintainable through clear separation of concerns.
3Productivity
If feature-based comparisons are used to search distributed code, then retrieval efficiency is improved, but loss of information about code context occurs
Solution Approach 1:
The patent transforms code into a different dimensional representation using embeddings that capture semantic meaning beyond simple feature matching. This dimensional transformation preserves contextual information while enabling efficient similarity search through vector space operations.
Data Source
AI summary
Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.