Synthetic Customer Transaction Data Generation System
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Solution Overview
Problem
Current software testing methods rely on subsets of real customer data, which may include personal identifiable information, posing risks and limitations in simulating real-world customer transaction data effectively.
Innovation Solution
A system and method for generating customer transaction test data that simulates real-world data by creating a dataset with fictitious information, using reference files to define account and transaction types, and generating random data records with specific formats and parameters, ensuring uniqueness and machine-generated certification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subsets of real customer data are used for software testing, then testing realism is improved, but data security and privacy protection deteriorate
Solution Approach 1:
The patent creates synthetic test data that copies the structure, format, and relationships of real customer data without using actual personal identifiable information. The system generates fictitious customer records, transactions, and accounts that mirror real-world data patterns, achieving testing realism while eliminating security risks associated with using real customer data.
Solution Approach 2:
The patent introduces an intermediary layer between real customer data and testing processes. Instead of directly using real data, the system processes real data through synthetic data generation algorithms that preserve data characteristics and relationships while removing all personally identifiable information, thus mediating between testing needs and security requirements.
2Object-affected harmful factors
If synthetic test data is generated, then data security is improved, but testing realism may deteriorate
Solution Approach 1:
The patent applies local quality by ensuring that different aspects of synthetic test data have different characteristics - the data structure, format, and relationships accurately reflect real-world patterns for testing realism, while the content is completely fictitious for data security. This localized application of quality standards allows the synthetic data to be realistic in form but fake in substance.
Solution Approach 2:
The patent changes key parameters of the data from real to synthetic while maintaining structural integrity. It transforms actual customer information into generated fictitious data, changes real transaction amounts to simulated values, and converts actual dates to generated timestamps, thereby maintaining testing realism through parameter preservation while achieving data security through parameter transformation.
3Adaptability or versatility
If comprehensive customer transaction data is generated, then testing coverage is improved, but data complexity and processing requirements worsen
Solution Approach 1:
The patent segments the test data generation process into distinct components: customer record generation, account creation, transaction generation, and data relationship establishment. By dividing the comprehensive data generation task into these manageable segments, the system achieves wide testing coverage while keeping processing requirements organized and manageable through modular data generation.
Solution Approach 2:
The patent performs preliminary actions by pre-defining data structures, relationships, and generation rules before creating the synthetic test data. It establishes the framework for customer accounts, transaction types, and data relationships in advance, which simplifies the subsequent data generation process and reduces processing complexity while enabling comprehensive testing coverage.
Data Source
AI summary
Systems and methods for generating customer transaction test data that simulates real world customer transaction data are disclosed. In one embodiment, a method may include (1) generating an account type reference file defining a plurality of account types; (2) generating a transaction type reference file defining a plurality of transaction types; (3) generating a plurality of customer test data records based on at least one of the plurality of parameters, wherein each customer test data record comprises a plurality of data fields, the data in each data field having a format that simulates real-world data; (4) verifying a uniqueness of each of the plurality of customer records; (5) generating a random number of accounts for each customer test data record based on the account type reference file; and (6) generating a random number of transactions for each account based on the transaction type reference file.