Benefits of Synthetic Data for Test Data Management

Creating quality test data is crucial for QA managers to create flawless applications. But it can take days to provision a single batch of real-world data.

Using synthetic data speeds up the process by providing realistic data at the touch of a button. Synthetic data also shields sensitive information and reproduces situations that are hard to find in real-world data.

GenRocket

GenRocket is an enterprise synthetic data generation platform that delivers centralized data modeling and distributed self-service for software test engineers. It replaces static copies of production data and augments with real-time synthetic data that is dynamically generated for each automated test run. This eliminates bottlenecks caused by manual data provisioning and enables teams to maximize test case coverage in order to prevent costly defects from making their way into production.

Unlike traditional TDM tools that require dedicated infrastructure for hosting and data storage, GenRocket is a light-weight Java runtime and repository with flexible REST and socket engines. Its architecture provides the flexibility to integrate directly into any test automation framework and CI/CD pipeline.

Synthetic data can be produced much faster than real data and is often a more cost-effective option for organizations, particularly in regulated industries like financial services and healthcare where sensitive or regulated information must remain confidential. Additionally, it is a valuable tool for creating perfect labeled data for supervised learning tasks in order to accelerate model development and reduce the time and expense associated with manually labeling real-world data sets.

Rule-Based Data Generation

Synthetic data is a key asset to businesses for three reasons: it’s fast, addresses privacy concerns and trains machine learning models without exposing real-world data. Privacy laws are strict, and sharing real-world data can expose businesses to costly lawsuits and brand damage.

A new approach to synthetic data uses symbolic and subsymbolic AI to combine rules and statistical information for generating realistic datasets that mimic the structure and characteristics of a specific source set, such as tabular data. It also provides insights and confidence with respect to sparse data regions, such as edge cases that can be difficult to test using traditional methods.

Businesses can use rule-based synthetic data to create a wide range of datasets, from structured data like tabular data to unstructured data such as images and videos, for use in analytics and modeling. This process eliminates the need to move data sets between development teams and allows developers to work at the pace they’re accustomed to, while providing better results for the end user.

High Volume Data Generation

In system testing, multiple inputs are required to emulate the real-world behavior of interconnected systems. AI-generated synthetic data provides high-quality test data for these types of tests.

Using synthetic data in test environments speeds up the time to refresh the test environment and reduces infrastructure costs for storing and managing the data. It also makes it easier to fulfill ticketed demand for data and minimizes stale, test-related defects that can affect quality.

With a complete test data management solution, teams can use existing data in production databases and fill in the gaps with synthetic data for maximum coverage. This data is then masked and subsetted to meet privacy regulations. This ensures that customer data is protected, but software teams can still access representative test data. This is critical for companies with regulated industries and/or customers who have strict security requirements. This approach is especially useful for Agile teams delivering quality at speed as they can design their own data to maximize test case coverage.

Felxibility

Synthetic data allows agile teams to realize the full potential of software applications, analytics activities and research projects without compromising the sensitive or confidential information in real-world datasets. It’s a powerful tool that can help protect against security and privacy risks while still providing valuable insights and enabling business growth and success.

Generating synthetic data with a generative model such as a GAN or Variational Autoencoder (VAE) produces high-quality, accurate, realistic and diverse test datasets for use in machine learning and other advanced applications. It also reduces bias and inaccuracy compared to real-world data.

It’s a cost-efficient alternative to traditional data collection methods and helps businesses comply with privacy regulations and other legal hurdles associated with authentic data sources. It can also be used to test the integrity of an organization’s internal data infrastructure and the effectiveness of policies and procedures. It can even be used to test the robustness of a software application or model against the impact of edge cases or outliers.