Contextual Re-ranking for Documentation Recommender Systems
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Solution Overview
Problem
Existing natural language processing (NLP) technologies face challenges in providing high-quality, up-to-date proprietary documentation to software developers, as search results often include outdated content and require significant user interaction, neglecting non-code information and user attention focus.
Innovation Solution
A dynamic and contextual ranking system that utilizes NLP to recommend reference documentation based on an interactively selected position in new source logic, incorporating non-code information and user attention focus to provide more relevant recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web search is used to find documentation, then a broad range of documentation can be accessed, but the search results are diluted with outdated content and non-relevant information
Solution Approach 1:
The patent introduces an intermediary system that sits between the user's code and the documentation search results. This intermediary analyzes the code context, cursor position, and surrounding code to filter and rank documentation results, thereby improving relevance while maintaining broad coverage through the underlying search engine.
Solution Approach 2:
The system dynamically changes search parameters based on code context. Instead of using fixed search queries, the system adjusts search terms, weighting, and filtering criteria based on the analyzed code context, cursor position, and surrounding code structure to optimize documentation relevance for each specific coding situation.
2Adaptability or versatility
If the entire source file is used as a search query, then comprehensive documentation can be found, but the cognitive load increases and irrelevant documentation is returned
Solution Approach 1:
The patent segments the search process into multiple stages: first analyzing the code context to identify relevant portions, then using only those specific segments as search queries rather than the entire file. This segmentation reduces the search space and cognitive load while maintaining comprehensive coverage of relevant documentation.
Solution Approach 2:
The system extracts only the necessary portions of code context that are relevant to the current editing position, rather than using the entire source file. This extraction process isolates the critical code elements surrounding the cursor position, creating a focused search query that reduces cognitive load while maintaining documentation coverage.
3Measurement precision
If code-to-code search is used, then code snippets can be found, but non-code information in documentation is neglected
Solution Approach 1:
The patent creates a universal search system that handles both code-to-code and text-to-text search simultaneously. The system analyzes both code elements and natural language documentation, using a unified ranking mechanism that considers relevance to both code snippets and explanatory text, thereby preventing loss of non-code information while maintaining code matching accuracy.
Data Source
AI summary
Here is dynamic and contextual ranking of reference documentation based on an interactively selected position in new source logic. A computer receives a vocabulary of lexical tokens, a sequence of references that contains a first reference to a first reference document before a second reference to a second reference document, respective subsets of the vocabulary that occur in the first and second reference documents, a new source logic that contains a sequence of lexical tokens, respective measurements of semantic distance between the new source logic and the first and second reference documents, and a selected position in the sequence of lexical tokens. Based on the selected position, the measurements of semantic distance are selectively increased. Based on that increasing the measurements of the semantic distance, a relative ordering of the first and second references is reversed to generate and display a reordered sequence of references.