Test Data Healing Through Automatic State Restoration
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Solution Overview
Problem
Organizations face challenges in maintaining data accuracy and consistency during testing due to changes in test data status, leading to inconsistencies and increased resource costs for manual data reconciliation.
Innovation Solution
A test data healing tool that automatically restores changed data to its original state using a machine-readable format, reducing the need for manual intervention and ensuring data consistency across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If test data is reused across multiple test scripts and sub-groups, then resource efficiency is improved, but data consistency deteriorates due to status changes
Solution Approach 1:
The system performs preliminary actions by detecting status changes in test data and automatically generating healing scripts before testing issues arise. The healing script is prepared in advance to restore test data to its original state, preventing consistency problems rather than reacting to them after occurrence.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring test data status changes and automatically triggering healing operations. When a status change is detected, the system feeds this information back into the testing workflow by executing healing scripts that restore data consistency, creating a closed-loop control system.
2Measurement precision
If manual data reconciliation is performed to maintain data consistency, then data accuracy is improved, but time consumption and resource costs increase
Solution Approach 1:
The system enables self-service by automatically detecting test data status changes and executing healing scripts without human intervention. The automated system serves itself by monitoring its own test data, identifying inconsistencies, and restoring consistency through self-generated healing operations.
Solution Approach 2:
The system replaces manual mechanical data reconciliation processes with automated computational mechanisms. Instead of testers manually comparing and correcting test data, the system uses automated status change detection, script generation, and execution to substitute human effort with machine-based operations.
3Adaptability or versatility
If test data status changes are allowed during testing, then testing flexibility is improved, but testing accuracy deteriorates due to inconsistencies
Solution Approach 1:
The system applies dynamics by allowing test data status to change during testing while maintaining the ability to restore it. The system dynamically monitors status changes, generates appropriate healing scripts based on the nature of changes, and executes restoration operations, creating a flexible yet controlled testing environment.
Solution Approach 2:
The system implements discarding and recovering by allowing test data to transition to changed states during testing, then automatically recovering the original state when needed. The healing process discards unwanted status changes and recovers the intended test data state, maintaining both flexibility and accuracy.
Data Source
AI summary
According to some embodiments, systems and methods are provided including a test data repository storing test data; a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to cause the system to: change a state of the stored test data from a first state to a second state in response to execution of test executable code, the execution using the first state test data; store the second state test data; detect a difference between the first state test data and the second state test data by comparing the stored second state test data to the first state test data; and restore the stored second state test data to the first state test data. Numerous other aspects are provided.