Introduction:
Let’s say you are developing a think pad for people who like to jot down their thoughts, plan their events, set goals, and maintain their overall calendar. But once you have made the draft, you are concerned about people’s privacy. What if people avoid using my app due to their concerns regarding the sensitive information they are sharing? But you also need to test your app using the data. What will you do? How are you going to test your app?
That creates the need for a trusted alternative i.e. synthetic test data generation, a technique that’s becoming increasingly important in software development.
By generating artificial data that accurately replicates real-world complexities while keeping privacy, it can both feed AI and ML models and eliminate privacy concerns. And with it developers can conduct thorough testing of software functionality, performance, and security. This approach builds user trust, protects sensitive data, and also ensures the quality of software.
In this article we will discuss how data scientists are implementing synthetic data for software testing and development.
The scope of synthetic data
According to Gartner, by 2030, artificial data will completely outshine real data testing, training and developing AI models. The synthetic data market is expected to grow at a CAGR of 7% from 2021 to 2027, reaching USD 1.15 billion.
To define synthetic data simply,
“Synthetic data is a masked version of real data. Just like you create a paper flower looking exactly like an actual flower with the same visual apparel, and it serves the purpose of decoration: A statistically resembling version of real data.”
With the explosion of machine learning models and advances in data science, data for testing has never been more important. Synthetic test data generation is on the rise to bridge the gap between real data availability and privacy concerns.
How is synthetic data different from real data?
- The first difference is the way in which the data is created. To collect real data, people conduct surveys and gather information from applications they use and provide their information to. On the other hand, synthetic data is generated artificially using machine learning techniques.
- The second difference is the data protection regulations that affect real data and synthetic data. With real data collection, certain concerns are attached to it, such as why you are collecting data, what the main purpose is, and how you are going to use it, etc. But with synthetic data, there is no such regulation attached because it doesn’t belong to an actual individual.
- The third difference is the amount of data available. In real data, you can only get as much data as users give you, while in synthetic data you can get as much as you want.
Why do we prefer synthetic test data for software testing?
- Unlike real user data, the synthetic data sets don’t contain any personal information (PII). This approach overcomes the drawbacks of using real data (which can raise privacy concerns) and anonymized data (which may lack the complex statistical relationships required for effective testing).
- Synthetic data has all the required statistical characteristics of real data. Because of this reason, the created artificial data is realistic and varied at the same time.
- It’s relatively cost-effective because you can create a much larger synthetic data set similar to the one you already have, which means your ML models will have much more data to work with.
- It automatically labels and cleans the data for you, so you don’t have to manually prepare the data for ML or analytics.
- Synthetic data helps overcome AI bias with the help of a data balancing process, in which you can increase the size of minority classes by inserting synthetic data samples containing minority classes.
Due to the above reason software developers can preferably use synthetic test data generation to create robust test environments while maintaining the highest privacy standards.
Methods to generate synthetic data
You can generate synthetic test data in three different ways:
- Rule-based generation
- Model-based generation
- AI/ML-based generation
Rule-based data generation
is based on predefined rules, algorithms, and data formats/constraints. This method is effective for generating basic data structures with predefined rules.For example, you can generate customer names based on predefined name formats and probability distributions for first names, last names, and first initials.
Model-based data generation
relies on statistical models trained from real datasets and captures the underlying relationships/patterns within the data.
Model-based data generation has the advantage of being able to replicate data with complex relationships between various attributes.
For example, financial transactions/customer behavior data.
AI/ML-based generation
Advanced techniques involve the use of artificial intelligence/machine learning algorithms. These algorithms are able to learn complex patterns/relationships from real datasets and generate highly accurate synthetic data that is very similar to the original.
Examples of machine learning models:
- Generative adversarial networks
- Variational autoencoders
Techniques that help generate synthetic data for software testing
When we visit an app, one of the first things we notice is the smoothness and speed of the application. This is because the smoothness and usability of any app directly depend on the quality of data used to test the application’s functionality.
Test data helps to verify that the application performs as it should. It can detect errors, bugs, and glitches that could disrupt the user experience. Creating test data is not a simple task. It takes time and effort. Testers spend nearly half of their time on data testing.
Here are some techniques that help developers create synthetic test data for software testing:
Open-Source Tools
Open-source tools provide open-source code for generating synthetic data sets that you can edit and improve to create your own solution.
These types of tools are budget-friendly and easy to use with the help of tutorials. Plus, there are several open-source communities that are also helping businesses operate these tools.
There are a number of open-source tools available in market. Of the 23 available open-source tools, these 8 are some of the best:
Copulas – Python library for multivariate distribution modeling and sampling with copula functions
CTGAN – SDV’s collection of synthetic data generators based on deep learning using single table data
DataGene – Tool for training, testing, and validating datasets, comparing and comparing dataset similarity with real datasets
DoubleGANger – Python framework for synthetic data generation based on Generative Adversarial Networks (GANs)
Wasserstein Generative Adverse Network (W-GAN) – Solution for Wasserstein Generative Anelevised Networks (WGAN) trained on real private dataset
DPyn – Algorithm for the synthesis of micro data while providing differential privacy
Faker – It is one of the most popular Python test data generation libraries that allows you to create synthetic data sets that are both real and editable.
Adata – It is an open-source Java library that allows you to create custom synthetic test data using user-defined distributions.
Commercial Software
Platforms and frameworks that integrate with your data pipeline and offer synthetic dataset generation and assessment functionality as-a-service.
Most commercial vendors offer some level of privacy guarantees. Which means they design the mechanisms in synthetic data to prevent the re-identification of a user from the original dataset.
Commercial vendors offer Software as a Service (SaaS), professional services, support and licensing on a monthly or annual basis. Some vendors provide free trials or plans.
Custom-Built Solutions
In some cases, organizations may opt to build their own synthetic data generation solutions. This approach enables highly customized solutions that meet specific testing needs and data structures. Custom development, however, requires considerable technical knowledge and resources.
Conclusion:
Cognilytica predicts that the synthetic test data generation market will reach USD 1.1 billion by 2027 from USD 110 million in 2021.
This shows how fast the software industry is taking turns. And the shift is evident. We are witnessing countless benefits of synthetic data in almost every industry. Like the flawless integration of synthetic data in testing AI and ML models, and its cost-effectiveness. The future belongs to those who survive the continuous evolution. Let’s see to what extent synthetic data will stand its ground and how it’s going to revolutionize data science more effectively.