Autonomous Software Feature Delivery via Agglomerated Models
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Solution Overview
Problem
Current testing and computer application generation technologies are inefficient, requiring significant manual effort and time, struggling to adapt to continuously evolving user stories and documentation, leading to incomplete model generation and limited utility in verifying desired functionality, and failing to effectively automate the process of generating and validating software features.
Innovation Solution
A system and method for autonomously constructing verified and validated agglomerated models of computing systems, which utilizes data and interactions to generate and update models, enabling automated testing, feature enhancement, and code generation, by connecting disparate data sources and applying machine learning techniques to improve model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually developed models or very detailed system requirements are used for testing, then testing accuracy can be improved, but the time and effort required for model creation and maintenance increases significantly
Solution Approach 1:
The system enables autonomous model generation and validation by having the computing system itself provide the data and functionality being tested. The system automatically creates models from its own operation data, eliminating the need for external manual model development while maintaining high testing accuracy through self-verification.
Solution Approach 2:
The system performs preliminary model generation and validation automatically during the testing process setup. By pre-generating models from available system data and pre-validating them against system functionality, the system eliminates the time-consuming manual model creation step while ensuring testing accuracy from the outset.
2Adaptability or versatility
If existing testing systems are used with continuously developed user stories and documentation, then adaptability to changing requirements improves, but model completeness and reliability deteriorate due to missing data and requirement inconsistencies
Solution Approach 1:
The system implements a feedback loop where generated models are automatically validated against the actual computing system functionality. This feedback mechanism identifies missing data and requirement inconsistencies, allowing the system to iteratively improve model completeness and reliability while adapting to continuously changing user stories and documentation.
Solution Approach 2:
The system dynamically generates and updates models based on current system state and available data. Rather than relying on static pre-defined models, the system adapts model generation to the current development environment, automatically adjusting to changing requirements while maintaining model reliability through continuous validation against actual system behavior.
3Reliability
If comprehensive testing coverage is achieved, then reliability of computing system verification improves, but manual labor and cost increase significantly
Solution Approach 1:
The system performs comprehensive model validation and testing autonomously using its own operational data and functionality. The computing system serves as both the test subject and the testing mechanism, eliminating the need for external manual testing efforts while achieving comprehensive verification coverage through automatic model generation and validation.
Solution Approach 2:
The system uses a unified autonomous model generation and validation approach that can test multiple aspects of computing system functionality simultaneously. Rather than requiring separate manual testing processes for different verification aspects, the system's multi-functional approach handles model generation, validation, and comprehensive verification through a single automated process.
4Measurement precision
If detailed functionality verification is performed, then measurement precision of desired functionality improves, but the complexity of the testing system increases
Solution Approach 1:
The system uses generated models as intermediaries between the testing objective and the actual computing system functionality. These models serve as simplified representations that capture essential system behavior, enabling detailed functionality verification through model validation rather than direct complex system testing. The models act as mediators that reduce testing complexity while maintaining verification precision.
Data Source
AI summary
A system for autonomously delivering software features is disclosed. The system parses data obtained from a variety of sources, and extracts source concepts from the parsed data to generate models for inclusion in a set of agglomerated models. Over time, additional data from the variety of sources may be utilized to update the set of agglomerated models. The updated agglomerated models may be analyzed by the system to determine whether new features and/or functionality may be added to an application under evaluation by the system. In the event that new features and/or functionality may be added to the application under evaluation, the system may automatically generate code corresponding to the new features and/or functionality and incorporate that features and/or functionality into the application under evaluation.