Navigational Semantics via Hypothesis Generation
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Solution Overview
Problem
Current testing technologies require substantial manual effort to understand software applications, build models with states, transitions, and constraints, leading to inefficiencies and inaccuracies in navigating and testing software systems, especially for complex systems where manual validation and verification are intractable.
Innovation Solution
A system and method for understanding navigational semantics through hypothesis generation and contextual analysis, utilizing data from software applications, internal and external documents, and natural language processing to determine constraints, order of operations, and transitions, with confidence values and a learning engine to refine and prioritize constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to build models with states, transitions, and constraints, then model accuracy can be maintained, but substantial manual effort and time are required
Solution Approach 1:
The system performs autonomous discovery of constraints, states, and transitions by automatically analyzing application outputs, documents, and execution traces without requiring manual model building. The learning engine continuously refines the model through hypothesis testing, enabling the system to self-improve accuracy while reducing manual effort.
Solution Approach 2:
The patent replaces manual mechanical processes of model building with automated computational processes including natural language processing, hypothesis generation, and machine learning algorithms that automatically extract navigational semantics from application data.
2Productivity
If automated testing systems are implemented, then productivity increases, but understanding navigational semantics and determining constraints becomes more difficult
Solution Approach 1:
The system introduces a learning engine as an intermediary that bridges automated testing and constraint determination. This learning engine uses hypothesis generation and testing to automatically discover constraints from application execution traces and documents, making the constraint determination process transparent and automated rather than difficult.
Solution Approach 2:
The system implements continuous feedback loops where test execution results feed back into the learning engine, which refines constraint models and generates improved hypotheses. This feedback mechanism automatically resolves the difficulty of constraint determination by learning from actual application behavior during testing.
3Use of energy by moving object
If brute force algorithms are used for boundary detection, then computational efficiency is improved compared to linear search, but guided systems that test high probability boundaries first would be more efficient
Solution Approach 1:
The system changes the approach from fixed algorithmic search (brute force or binary search) to dynamic parameter-based exploration. The learning engine adjusts testing parameters based on probability distributions derived from execution traces and document analysis, prioritizing high-probability boundaries and constraints for testing, thereby reducing both computational energy and time requirements.
4Adaptability or versatility
If models are continually updated to reflect rapidly evolving software solutions, then adaptability increases, but maintenance efforts become not cost effective
Solution Approach 1:
The learning engine provides self-service by automatically detecting when software changes occur through continuous monitoring of application execution traces. It autonomously updates the constraint model to reflect new behavior without requiring manual intervention, maintaining adaptability while eliminating costly maintenance efforts.
Solution Approach 2:
The system implements continuous model updating through ongoing analysis of execution traces and documents rather than periodic manual updates. This continuous automated process maintains model adaptability to evolving software while reducing maintenance costs by eliminating the need for manual model revisions.
Data Source
AI summary
A system for understanding navigational semantics via hypothesis generation and contextual analysis is disclosed. The system may, such as when examining and testing a software application, address the handling and resolution of constraint hypotheses in an uncertain environment, where potentially overlapping or conflicting suggestions are generated with various confidences. The system may utilize algorithmic and/or machine learning tools to identify consistent constraints for the software application with the highest levels of confidence. During operation, the system may continuously perform hypothesis testing on constraints generated by the system, which may result in the creation of new hypotheses yielding improved confidences. Feedback from the hypothesis testing may be provided to knowledge sources to improve the processing of information subsequently processed by the system. The system may construct complex constraints on multiple fields or functional transitions with associated confidences. A constraint optimizer of the system may simplify constraints or reduce their quantities.