Semantic Relationship Discovery in Software Systems
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Solution Overview
Problem
Current systems fail to effectively and efficiently discover semantic relationships between disparate data types, such as video, audio, and text, and cannot handle time or action-sensitive relationships, leading to sparse, noisy, and error-prone results in software testing and navigation.
Innovation Solution
A system that uses supervised learning techniques to identify concepts, relationships, and groupings by combining organizational, geometrical, and language input sources, employing reasoners to generate and verify hypotheses, and optimizing feature selection for accurate semantic relationship discovery, including natural language processing to determine constraints and transitions in software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems rely on source information structure to develop semantic data associations, then the system can process structured data, but the relationships produced are sparse, noisy and error prone
Solution Approach 1:
The system combines multiple information sources including source code structure, rendered visual structure, textual information, and audio information to develop semantic relationships. This merging of diverse data types enriches the relationship discovery process and reduces sparsity and noise in the produced relationships.
Solution Approach 2:
The system is designed to handle multiple types of data and relationships universally, not limited to single data type analysis. It can process code structure, visual displays, text, and audio simultaneously, making the semantic relationship discovery more robust and less error-prone.
2Ease of operation
If systems visually examine rendered structure to develop data associations, then the system can segment displayed information, but the system cannot resolve data relationships lacking clear visual hierarchy
Solution Approach 1:
The system merges visual structure analysis with source code structure analysis and textual analysis. When visual hierarchy is ambiguous or absent, the system can fall back on source code relationships or textual context to resolve data associations, thereby handling relationships lacking clear visual hierarchy.
Solution Approach 2:
The system introduces textual information and source code structure as intermediary sources when visual structure is insufficient. These intermediaries provide alternative pathways to establish semantic relationships when visual hierarchy alone cannot resolve the relationships.
3Productivity
If systems examine single domain of information to establish semantic relationships, then the system can process that specific data type, but the system cannot effectively relate information across disparate data types
Solution Approach 1:
The system is designed with multi-functionality to process and relate multiple data types including code, visual displays, text, and audio within a unified framework. It maintains efficiency for each data type while enabling cross-domain relationship discovery through the integrated analysis architecture.
Solution Approach 2:
The system merges analysis of disparate data types by integrating source code parsing, visual structure analysis, natural language processing, and audio transcription. This combination enables the system to establish semantic relationships across different data types that would be impossible with single-domain analysis.
4Measurement precision
If systems rely on structural analysis of source code, then the system can identify explicit relationships, but the system cannot draw connections between information whose associations cannot be directly derived from structure
Solution Approach 1:
The system combines structural analysis with textual analysis and visual analysis to capture both explicit and implicit relationships. While structural analysis provides precise explicit relationships, the integration of text and visual contexts reveals implicit relationships that cannot be derived from structure alone.
Solution Approach 2:
The system performs multiple types of analysis (structural, textual, visual, audio) beyond what a single analysis method would provide. This excessive action ensures that both explicit and implicit relationships are captured, compensating for the limitations of any single analysis approach.
Data Source
AI summary
A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing. Information pertaining to validated or invalidated hypotheses may be provided to a learning engine to maximize reasoning and performance in subsequent discovery processes by adjusting models, vocabularies, dictionaries, parameters utilized by the system in identifying the relationships, concepts, and groupings.