Software Code Mapping via Icon Functionality Correlation
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Solution Overview
Problem
Existing software code searching technologies fail to effectively map and reuse software code across different user interfaces, leading to redundant development and inefficiencies due to semantic differences in icon labels, which hinder the identification of equivalent functionalities.
Innovation Solution
A system and method that maps software code based on user interface functionality by constructing probabilistic classifiers and using deep learning to analyze and correlate the functionality of icons across different interfaces, allowing for the identification of equivalent functionalities despite semantic differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software code searching uses traditional keyword matching, then the search process is simple and fast, but it fails to identify equivalent functionalities due to semantic differences in icon labels
Solution Approach 1:
The patent introduces user interface functionality descriptions as an intermediary layer between the search query and the software code database. Instead of directly matching keywords to code, the system extracts and compares functionality descriptions that semantically represent what the code does, enabling accurate identification of equivalent functionalities across different interfaces while maintaining system manageability through modular architecture
Solution Approach 2:
The patent replaces traditional mechanical keyword matching mechanisms with deep learning-based semantic analysis. The system uses trained neural networks to understand and compare the meaning of functionality descriptions, allowing it to identify equivalent functionalities even when the exact keywords or labels differ, thereby achieving high matching accuracy without requiring complex manual rule-based systems
2Adaptability or versatility
If software code searching analyzes semantic meaning of icons, then equivalent functionalities can be identified across different interfaces, but the search process becomes time-consuming and computationally intensive
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user interface functionality descriptions in the software code database during database construction. The system extracts functionality descriptions from user interfaces in advance and stores them alongside the code, so that during search operations, the system only needs to compare pre-extracted descriptions rather than performing complex semantic analysis in real-time, significantly reducing search time while maintaining cross-interface identification capability
Solution Approach 2:
The patent uses copying by creating and storing standardized functionality description representations of user interfaces in advance. These copied functionality descriptions serve as searchable proxies that capture the essential meaning of icons and interfaces without requiring the original complex interface structures to be analyzed during search, enabling fast comparison and matching across different interfaces
Data Source
AI summary
Techniques for identifying similar software code are provided. In one example, a computer-implemented method comprises: based on detection of an input, determining, by a device operatively coupled to a processor, a user interface functionality associated with a website; and based on a likelihood that the user interface functionality and a result of a query have a defined level of correlation, matching, by the device, the result of the query to the user interface functionality. The computer-implemented method can further comprise mapping, by the device, a vector associated with the website, to an integer value, employing a hash function.