Code Context Analysis for Automatic Reference Data Identification
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Solution Overview
Problem
Programmers face inefficiencies in finding relevant reference data in forums and repositories due to the need to manually craft search terms, which does not leverage the context provided by their own code.
Innovation Solution
A method where a processor analyzes sections of code, computes context, generates search terms, and automatically displays relevant reference data by searching databases and forums, utilizing context-based queries and knowledge graphs to streamline the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If programmers manually craft search terms to find reference data in forums and repositories, then they can locate relevant information, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs self-service by automatically analyzing the programmer's code and generating search terms without requiring manual input. The processor extracts context from the code sections, identifies relevant keywords, and queries databases autonomously, allowing the system to serve itself rather than requiring the programmer to manually craft search queries.
Solution Approach 2:
The system performs preliminary action by pre-analyzing the code structure and pre-generating search terms before the programmer actually needs to search for reference data. The context computation and keyword extraction happen automatically as the code is being written or reviewed, so that when the programmer needs reference information, the search is already prepared and can be executed immediately.
2Loss of information
If programmers manually search for reference data, then they can find relevant information, but the search process requires significant effort and skill
Solution Approach 1:
The system introduces an intermediary processor that acts as a mediator between the programmer's code and the databases/forums. Instead of the programmer directly searching through multiple databases and forums, the processor analyzes the code, generates appropriate search terms, and queries the relevant databases automatically, simplifying the complex search process into a single automated operation.
Solution Approach 2:
The system achieves universality by creating a multi-functional processor that can analyze different types of code sections, compute various contexts, generate multiple search terms, and query different databases and forums through a single unified interface. This universal system handles diverse programming tasks and data sources without requiring separate tools or manual processes for each.
3Productivity
If the system automatically generates search terms from code context, then the search efficiency improves, but the system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the automated search process into distinct modular components: code analysis module, context computation module, search term generation module, and database querying module. Each module performs a specific function and can be independently developed and maintained, reducing the overall system complexity despite the advanced functionality provided.
Data Source
AI summary
A processor may analyze one or more sections of code within a program. The processor may compute a context for each of the one or more sections of code. The processor may generate one or more search terms that are respectively associated with each of the one or more sections of code. The processor may automatically display reference data to a user.