Serialized Tree Structures for Fast Code Similarity Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional tree-comparison and/or tree-distance computation algorithms are inefficient and resource-intensive, often obscuring certain types of tree similarities and leading to slow comparison operations in applications like enterprise software and AI.
Innovation Solution
The method involves serializing tree structures into mathematical sets using an enumeration method, allowing for fast and efficient set operations with metric functions to calculate similarities or distances, while ignoring sibling node ordering to identify structural similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional tree-comparison algorithms are used, then comparison operations can be performed on tree structures, but the operations are prohibitively slow and computing-resource intensive
Solution Approach 1:
The patent transforms the tree structure representation by serializing it into a flat array format with specific metadata fields (node type, depth, sibling index, etc.). This parameter transformation enables the use of efficient array-based operations and hashing instead of complex tree traversal, dramatically improving comparison speed and reducing computing resource consumption while maintaining the ability to detect structural similarities.
2Measurement precision
If conventional tree-comparison algorithms are used, then tree similarities can be identified, but certain types of tree similarities are problematically obscured among the associated structures and data
Solution Approach 1:
The patent segments the tree comparison task into distinct metadata fields (node type, depth, sibling index, array index) that can be independently analyzed. This segmentation allows the system to detect similarities at multiple levels of the tree structure simultaneously, preventing obscuration of similarity patterns that would be invisible in conventional holistic comparison approaches.
Solution Approach 2:
The patent applies metaphorical 'color changes' by transforming the tree structure into a standardized serialized format with specific metadata tags that highlight different structural aspects. This transformation makes previously hidden similarity patterns visible and detectable, analogous to how color changes make invisible patterns visible to the human eye.
3Measurement precision
If tree structures are compared with sibling node ordering information, then exact structural matches can be identified, but similarities between trees with different sibling orderings are obscured
Solution Approach 1:
The patent extracts sibling node ordering information from the tree structure during serialization, storing it in a specific metadata field (sibling index) that can be independently handled. This extraction allows the comparison algorithm to optionally ignore ordering differences while maintaining the ability to detect structural similarities, providing adaptability without sacrificing precision when ordering matters.
Data Source
AI summary
Example embodiments facilitate efficient comparison operations of tree structures, resulting in comparison metrics (e.g., similarity or distance metrics or scores) used enhance software systems, such as search algorithms, code optimization software, enterprise database applications, and so on. Trees to be compared are converted into sets, i.e., serialized using a novel enumeration method. Metric functions can then be efficiently applied to the sets to facilitate the comparison operations. In an illustrative embodiment, subtrees of larger trees can be compared individually, pairwise, where the comparison results of the subtree comparisons can be selectively weighted and summed to yield an aggregated comparison metric that is tailored for a specific application or comparison priority.