Automated Test Data Generation and Transformation via Natural Language Queries

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Solution Overview

Problem

The existing test data generation process is labor-intensive and repetitive, requiring technical knowledge to fetch data from multiple sources and transform it to meet target system requirements, which slows down the test lifecycle and increases costs.

Innovation Solution

A system and method that receives user queries in natural language, parses them to generate keywords, creates data source-specific executable queries, executes them across various data sources, identifies missing data, and transforms the data to comply with target system requirements, using a cognitive learning engine and meta-data creation module.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If test data is manually fetched from multiple source systems through structured queries, then test data can be obtained, but the process is labor-intensive and time-consuming

Engineering Contradiction:

Improvetest data generation speedVSAvoidtime to source test data

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating test data through executable queries without requiring manual intervention. The cognitive learning engine autonomously creates queries, executes them against source systems, and transforms the retrieved data, eliminating the need for testers to manually fetch data from multiple sources.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of writing and executing structured queries is replaced by an automated cognitive learning engine that generates executable queries programmatically. This substitution transforms the labor-intensive manual operation into an automated computational process, significantly improving productivity and reducing time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If testers possess required technical knowledge to fetch test data, then data can be retrieved from multiple sources, but the barrier to entry increases and operational complexity rises

Engineering Contradiction:

Improveease of test data retrievalVSAvoidtechnical knowledge requirement

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The cognitive learning engine acts as an intermediary between the tester and the source systems. Instead of requiring testers to directly interact with complex structured queries and multiple data sources, the intermediary automatically generates and executes queries, transforming the complex technical operation into a simple user-friendly process.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system provides universal functionality by handling multiple source systems through a single unified interface. The cognitive learning engine can generate executable queries for different database types and formats, eliminating the need for testers to learn system-specific query syntax and making the operation accessible to users with minimal technical knowledge.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Manufacturing precision

If test data is transformed to meet target system requirements, then data compliance is achieved, but the transformation process is repetitive and labor-intensive

Engineering Contradiction:

Improvedata definition complianceVSAvoidtest data transformation speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system performs preliminary action by pre-defining transformation rules and mappings between source and target data formats. The cognitive learning engine learns the required transformations in advance and automatically applies them during data retrieval, eliminating the need for repetitive manual transformation operations while maintaining precise compliance with target system requirements.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If manual test data management is used, then flexibility is maintained, but cost and time to market increase

Engineering Contradiction:

Improvetest lifecycle efficiencyVSAvoidtime to market

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The manual mechanical test data management process is replaced by an automated cognitive learning engine that generates, retrieves, and transforms test data programmatically. This substitution eliminates the time-consuming manual operations while maintaining the flexibility to adapt to different source and target systems, thereby reducing time to market without sacrificing operational flexibility.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP3352098B1Method of generating and transforming test data and a system therefor

Publication Date: 2021.07.21 WIPRO LTD

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AI summary

The present disclosure relates to a method and system for generating and transforming test data. In one embodiment, a user query is received in natural language and parsed to generate keywords using lemmatization. Based on the generated keywords and filter conditions in the user query, a data source specific executable query suitable for data sources is generated and executed against each data source to generate test data. The method determines if there are any missing test data in the generated test data and creates missing test data based on the data type, number of records required. The method also automatically transforms the generated test data into corresponding test data suitable to the requirements of a target system. Thus, the system generates test data specific to different data sources based on query provided in natural language and transforms the generated test data to comply with the requirements of the target system.