Machine-Learning Code Notebook for Semantic Flow Search
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Solution Overview
Problem
Existing code analysis tools require precise constraints and are limited to expert users, failing to provide semantic and structural searching capabilities, making it difficult for non-experts to efficiently navigate and understand complex software code.
Innovation Solution
A code notebook that utilizes machine learning to enable semantic, structural, and programming-flow queries, allowing users to submit fuzzy queries that are expanded to include semantically similar terms and structural constraints, with results visually highlighted in the code representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search tools are used to search for code, then search precision can be maintained, but ease of operation deteriorates because experts must know exact variable, type, or routine names
Solution Approach 1:
The patent introduces an intermediary system that sits between the user and the codebase, automatically analyzing code structure, data flow, and variable relationships. This intermediary translates imprecise natural language queries into precise code searches, eliminating the need for users to know exact variable or routine names while maintaining search accuracy through automated code understanding.
Solution Approach 2:
The patent replaces the mechanical system of exact keyword matching with an intelligent system that uses natural language processing and code analysis. Instead of requiring precise mechanical matching of variable names and types, the system substitutes this with semantic understanding and automated code traversal, allowing users to search using everyday language while maintaining precision through backend analysis.
2Ease of operation
If code analysis tools require precise constraints, then measurement precision is improved, but ease of operation deteriorates because only expert users can effectively use them
Solution Approach 1:
The patent implements self-service by enabling the code analysis system to automatically generate and refine search queries without requiring expert user input. The system autonomously analyzes code structure, infers relevant variables and relationships, and formulates precise analysis queries, making expert-level code analysis accessible to non-experts while maintaining high precision through automated reasoning.
Solution Approach 2:
The patent applies preliminary action by pre-analyzing the codebase to build a comprehensive understanding of code structure, data flow, and variable relationships before user queries are submitted. This preliminary analysis creates a knowledge base that enables the system to rapidly respond to user queries with precise results, eliminating the need for users to perform complex preliminary analysis themselves.
3Productivity
If traditional search tools are used, then device complexity is minimized, but productivity deteriorates because experts spend excessive time searching for code
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing code analysis results, including data flow relationships, variable dependencies, and structural information, in an organized knowledge base. This preliminary processing enables rapid query response times, dramatically improving code navigation speed while the added complexity is confined to the backend infrastructure rather than affecting user-facing simplicity.
Data Source
AI summary
A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows-using. The code database is embedded with indications of semantic equivalence for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.