Source Code Figment Modeling for Implicit Segments
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Solution Overview
Problem
Existing coding tools face difficulties in representing and supporting multiple programming languages, as they struggle to understand the syntax and semantics of source code, especially when dealing with hidden or implicit code segments that lack a source location, which hinders effective tool support and static analysis.
Innovation Solution
A system and method for modeling source code using a data graph model that indexes code segments, creates nodes and edges to represent code elements and relationships, and generates figment representations for hidden or implicit code segments, allowing for a language-neutral, canonical representation of source code and compiler metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single code model is used to represent all programming languages, then tool support across languages is improved, but the data model complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the source code and the data model. This intermediary processes and standardizes code representations, allowing the same data model structure to handle multiple programming languages without requiring language-specific complexity in the core model.
Solution Approach 2:
The data model is designed with universal structures that can represent code elements from any programming language. By creating a language-agnostic representation system, the model achieves multi-functionality across different languages while maintaining a consistent structure, reducing the need for language-specific adaptations.
2Device complexity
If hidden or implicit code segments are excluded from the data model, then model simplicity is maintained, but code analysis accuracy deteriorates
Solution Approach 1:
The patent creates figment representations as copies or proxies for hidden and implicit code segments. These figments replicate the essential characteristics and relationships of the original invisible code elements, allowing them to be included in the data model without adding complex processing logic. This enables accurate representation of implicit code while maintaining model simplicity.
3Measurement precision
If figment representations are created for all hidden code segments, then code representation accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent applies figment representations selectively rather than universally. By identifying which hidden code segments require figment creation based on their significance and impact on code analysis, the system creates figments only where necessary. This localized approach maintains high representation accuracy for critical elements while avoiding unnecessary processing overhead for less important segments.
Data Source
AI summary
A system and method for modeling code segments that do not have a location is disclosed. Source code may be indexed and modeled in a data graph with nodes representing code elements and edges representing relationships between nodes. However, some code elements may be hidden or implicit and therefore may lack location information. In these cases, code figments are created and represented as nodes in the graph. Figment nodes may be specially designated so that the figment nodes may be easily distinguished from real source code nodes. The graph is then updated to include location information for the code figments in the nodes that interact with the hidden or implicit code. The data graph may then be provided to a user or as a service to be used by coding tools.