Reliable test data can be just as important to software testing as having effective test scripts. Even very good scripts will not find bugs when given a bad test dataset. Relying on production data presents the next biggest pitfall.
Production data often includes customers’ names, account and payment details, medical histories, and various other personal or confidential data. Synthetic data provides artificial data points that are structured, related, and conditioned to satisfy testing needs. This allows developers to test application behavior without using real customer data and to craft test cases that don’t exist in production data.
There are multiple open-source synthetic data generation tools available to help generate test data for software development and QA. But different kinds of tests need different kinds of fake data. Some tools generate simple dummy values; others include complex, interconnected tables and relational databases.
5 Best Open-Source Synthetic Data Generation Tools
Here are five useful options and circumstances in which to use them.
Faker: A Practical Tool for Everyday Test Data
Faker is a popular Python package for generating fake data. It generates names, addresses, emails, dates, phone numbers, text, etc. Seeded generation is supported by Faker, enabling a test engineer to generate identical output across successive test executions.
Faker can be effective, especially for regular application testing. Take an e-commerce registration form as a sample. A QA engineer may be required to register or create accounts for 500 users to fulfill their requirement.
Manual entry of these users is time-consuming, but with Faker, the process is automated. However, Faker’s strength lies primarily in generating unique fake values. It isn’t focused on complex discovery of correlations in raw production datasets as much as other tools might be.
So, organizations whose data’s relationships may be of special interest would need something more specific.
Mimesis: Useful for Fast and Localized Test Data
Mimesis is another fake data generation library in Python. It covers personal details, addresses, dates, monetary figures, telephone numbers, internet data, and various other types. Mimesis covers multiple languages/locales; this adds more potential applications to the library.
Imagine an online shop with international customers. If its QA personnel require a test file comprising customer profiles for users from the United States, France, Germany, and beyond. Data by locale will help properly test country-specific formats, such as those used in addresses and telephone numbers.
Mimesis is suitable for generating JSON or XML file contents, databases, and test application fixtures. Thus, suits cases which require significant data structures and where such information does not have overly complicated relationships.
SDV: Better for Complex Tabular Data
SDV, or Synthetic Data Vault, is a more advanced approach to generating synthetic tabular data. It is not limited to generating irrelevant random data; it can also learn patterns from existing datasets. SDV supports single, related, and sequential datasets, with additional useful Synthetic Data Generation Tools for constraint management, anonymization, preprocessing, and evaluation.
Those capabilities are crucial in cases where field dependencies are important. As in hotel reservation systems, reservation prices may be influenced by room type, booking season, booking time, and duration of stay. Using random generation would result in combinations that make less sense.
SDV, however, models the dependencies between fields using existing data so that new synthetic data can capture those statistical dependencies appropriately. Hence, SDV is well-suited to database testing, as it generates data with realistic statistical patterns. But each synthetic dataset still needs to be validated against essential business rules.
CTGAN: Designed for Complex Tabular Datasets
CTGAN is another method of synthesizing tabular information. CTGAN trains a conditional generative adversarial network to capture patterns of a structured dataset. An issue in training business databases that contain both continuous and discrete data is that unusual relationships may arise.
In an insurance testing scenario, information may include customer age, location, policy type, premium amount, and claim value. There are likely some relationships between some/all of those variables. CTGAN captures patterns from the underlying data and can generate artificial data that mirrors these relationships. This is useful as data-intensive applications can test these features.
Nevertheless, the statistical relationships in synthesized data differ from those in business-based data. The QA team should always validate the data in the system for business accuracy, such as whether dates are logical or whether the generated monetary amounts fall within a practical range.
DeepEcho: An Option for Time-Series Data
Not everyone’s app consumes just static records. In finance, monitoring, connected devices, and analytics apps, data may consist of multiple time-series datasets in which values vary across hours, days, and weeks.
DeepEcho specializes in synthetic generation for mixed-type and multivariate time-series datasets. With time-series artificial data generation, teams can test applications that process ordered or dynamic values. For example, if you’re evaluating software for managing machines in an industrial production facility, you could test a configuration under different temperature and pressure levels over a time-series.
Unlike static information, artificial time-series generation provides far more realistic test circumstances for the software program you’re analyzing. To ensure everything functions as expected before you expand the use of this tool, run all your tests.
How Should QA Teams Choose the Right Tool?
The best synthetic data generation tool depends entirely on the data and testing goals of your particular use case. Hence, teams should start by outlining the exact situations they want to test. Whether they represent basic data structures or relations among names, addresses, and basic database tables, they can be well served by Faker and Mimesis.
SDV is a better option for data with highly significant relationships between fields, while CTGAN is well-suited for teams that need to discover complex patterns in tables. For use cases targeting time-series data with specific needs, DeepEcho could also be relevant. The priority is to design tests to cover scenarios, not data volume. It’s rarely helpful for teams to have a million blank records, as they fail to illustrate an important boundary condition.
When Do QA Teams Need More Than an Open-Source Tool?
Many types of tests can benefit greatly from open-source synthetic data generation tools. While that’s a powerful concept, bigger QA teams often crave more insight into their complex data and connected systems. GenRocket provides on-demand synthetic test data generation to better fit enterprise testing and automation needs.
Conclusion
Synthetic data allows QA teams to gain greater control over their test environments. It means that instead of constantly relying on copies of the production environment, test teams can formulate datasets that mirror actual app usage. Fake data-via Faker and Mimesis- works well for basic needs.
With SDV, tables will receive greater support; CTGAN may offer more complicated statistics when combined with structured tabular data. Applications with time-series needs will have another avenue through DeepEcho. Even with this, start the technology choice by test objective.
It starts with determining whether your desired tests require certain relationships, edge scenarios, data volumes or specific privacy constraints. Once you have the necessary test parameters, you can select the technology that best generates and meets the expected conditions. It helps us uncover issues before users encounter them.
