Software Experimentation System Using Signature-Based Component Discovery
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Solution Overview
Problem
Current software experimentation systems lack a unified and extensible method to register and discover software elements, leading to inefficient resource consumption and erroneous results when testing various software components such as user interfaces, APIs, and machine learning models, as they often fail to perform comprehensive experiments across different types of software elements.
Innovation Solution
A software experimentation system that receives and stores feature variants with generated signatures, allows users to input experiment information, identifies compliant signatures, defines segments and metrics, and generates experiments to execute and analyze results, enabling efficient testing across multiple software components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current software experimentation systems test each software component separately without a unified method, then the system complexity remains low, but resource consumption increases and experimentation coverage decreases
Solution Approach 1:
The patent implements a universal experimentation system that can handle multiple types of software components (user interfaces, APIs, machine learning models, etc.) through a single unified framework. The system uses standardized schemas and signature generation to accommodate diverse component types, enabling comprehensive experimentation across the entire software stack without requiring separate testing systems for each component type.
Solution Approach 2:
The patent segments the software experimentation process into distinct modular components: schema definition, signature generation, experiment configuration, execution, and analysis. Each software component type is broken down into identifiable elements that can be independently registered, discovered, and tested. This segmentation allows the system to manage complexity through structured organization while maintaining comprehensive coverage.
2Reliability
If comprehensive experimentation across all software components is performed, then resource consumption increases, but the accuracy and reliability of results improve
Solution Approach 1:
The patent performs preliminary actions by generating signatures for all software components before experimentation begins. These signatures pre-characterize each component's interface, behavior, and dependencies, allowing the system to efficiently plan and execute experiments without redundant analysis during runtime. This preliminary structuring enables comprehensive testing while optimizing resource usage during actual experimentation.
Solution Approach 2:
The patent creates simplified representations (signatures and schemas) that copy the essential characteristics of complex software components. Instead of directly testing every aspect of each component, the system uses these copied representations to model component behavior and interactions, reducing the computational resources required for comprehensive experimentation while maintaining result accuracy.
3Productivity
If a unified method for registering and discovering software elements is implemented, then experimentation efficiency improves, but the initial system setup and complexity increase
Solution Approach 1:
The patent employs parameter-based schemas that define software components through configurable parameters and attributes. By changing and adjusting these parameters, the system can adapt to different component types and experimentation scenarios without requiring fundamental system changes. This parameter-driven approach enables efficient experimentation setup while managing complexity through standardized configuration rather than structural complexity.
Data Source
AI summary
A device may store, in a data structure, a plurality of feature variants associated with a schema of software and signatures generated based on the schema, and may provide a user interface that requests experiment information. The device may receive, via the user interface, the experiment information, and may identify, in the data structure, a set of feature variants, from the plurality of feature variants, based on the experiment information. The device may identify a set of corresponding signatures for the set of feature variants and may compare signatures of the set of corresponding signatures to identify compliant signatures of the set of corresponding signatures. The device may generate compliant feature variants based on the compliant signatures and may define segments and metrics. The device may generate the software experiment based on the compliant feature variants, the segments, and the metrics, and may execute the software experiment to generate results.