Code Radar Using Attention Matrices for Source Navigation
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Solution Overview
Problem
Conventional large language models discard attention matrices after generating source code completions, missing an opportunity to provide valuable context for source code navigation within integrated development environments (IDEs).
Innovation Solution
Utilizing attention matrices to generate mappings of token relevance, which are then used to provide a 'code radar' user interface experience, highlighting relevant source code locations to the user, including documentation, variable declarations, and assignments, by leveraging self-attention architectures and postprocessing techniques like attention mean, max, rollout, and follow-up attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If attention matrices are discarded after generating source code completions, then computational resources are freed, but valuable context information for source code navigation is lost
Solution Approach 1:
The system recovers and reuses attention matrices that would otherwise be discarded after code completion generation. By storing these matrices and applying postprocessing techniques (attention mean, max, rollout, follow-up attention), the system extracts meaningful token relationships to power the code radar navigation feature, thus recovering valuable information from previously discarded computational artifacts.
2Ease of operation
If attention matrices are processed to generate token relevance mappings, then source code navigation context is improved, but computational overhead increases
Solution Approach 1:
Instead of processing all attention matrix data equally, the system applies selective postprocessing techniques to extract the most relevant token relationships. The code radar feature processes only the necessary portion of attention information to provide effective navigation context, avoiding unnecessary computational overhead while maintaining navigation quality.
3Measurement precision
If multiple postprocessing techniques are applied to attention matrices, then mapping accuracy is improved, but processing time increases
Solution Approach 1:
The system precomputes and stores multiple types of attention mappings (mean, max, rollout, follow-up attention) during the code completion generation phase. These preprocessed mappings are then readily available for rapid retrieval during code navigation, eliminating the need for real-time computation and reducing processing delays when the code radar feature is activated.
Data Source
AI summary
Using a language model attention matrix to facilitate a “code radar” source code navigation experience that highlights related source code locations. A computer system identifies a first source code location within source code that is displayed at a code editor user interface (UI). From a set of mappings generated based on a language model attention matrix, the computer system identifies a second source code location as being related to the first source code location. Concurrent with presenting the first source code location in the code editor UI, the computer system presents a related source code navigation experience, which includes both (i) presenting the second source code location in the code editor UI, and (ii) presenting a visual indication that the second source code location is related to the first source code location. Some embodiments include generating the set of mappings based on a language model attention matrix.