Decision Algorithm Testing Platform Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing software development systems lack the ability to efficiently switch between test and production environments for decision algorithms, leading to potential data corruption and inefficient testing and deployment processes.
Innovation Solution
A software development platform that provides a management interface for client devices to toggle between test and production modes, allowing decision algorithms to be tested on segregated test data without affecting live production data and subsequently deployed to production environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If decision algorithms are tested directly on production data, then testing can be performed immediately, but data corruption and loss of reliability occur
Solution Approach 1:
The system segments the data environment into distinct test and production databases, allowing decision algorithms to be tested on test data without accessing production data. This segmentation prevents data corruption while enabling efficient testing, resolving the contradiction between testing productivity and data reliability.
Solution Approach 2:
The system introduces an intermediary switching mechanism that controls access to different databases. The switch acts as a mediator between the decision algorithm and the databases, routing test operations to test data and production operations to production data, thereby protecting production data integrity while enabling testing.
2Reliability
If separate test and production environments are maintained, then data reliability is preserved, but system complexity and deployment time increase
Solution Approach 1:
The system implements a universal decision engine that can operate with either test data or production data based on the switch configuration. This multi-functionality allows the same engine to serve both testing and production purposes, reducing the need for separate environments and simplifying system management while maintaining data reliability.
Solution Approach 2:
The system introduces a dynamic switching mechanism that allows flexible transition between test and production modes. This dynamic configuration enables the system to adapt its data source based on operational requirements, making environment management more flexible and less complex while preserving data integrity through proper routing.
3Adaptability or versatility
If decision algorithms are modified frequently for testing, then adaptability improves, but risk to production data increases
Solution Approach 1:
The system performs preliminary testing of modified decision algorithms on test data before deploying them to production. This preliminary action allows frequent algorithm modifications and testing without exposing production data to potential harm, as all modifications are first validated in the isolated test environment.
Solution Approach 2:
The switching mechanism serves as an intermediary that controls the deployment flow of modified algorithms. It ensures that only algorithms successfully tested and validated in the test environment can be switched to production mode, thereby mediating between the need for frequent modifications and the need to protect production data from harmful effects.
Data Source
AI summary
This disclosure involves development and deployment platforms for decision algorithms. For example, a computing system provides software development interface to a client device. The system sets, based on an input from the client device via the interface, a decision engine to a test mode that causes the decision engine to operate on test data stored in a first database and that prevents the decision engine from applying operations from the client device to production data stored in a second database. The system also configures the decision engine in the test mode to execute a different decision algorithms on the test data. The system also sets, based on another input via the interface, the decision engine to a deployment mode that causes the decision engine to operate on the production data. The system configures the decision engine in the deployment mode to execute one or more of the tested decision algorithms.