This document will give you enough information about the importance of test data, the existing technique to create test data, and the disadvantage of that techniques. Next, we focus on what is synthetic data? How can we generate it? How is AI/ML is using to create synthetic data? What are the benefits and applied areas? Finally, we will go over the definition of ISDG platforms, how it establishes synthetic data, and its feature.
Why Test Data?
Test data in software testing is the input given to a software program during test execution. It represents data that affects or is affected by software execution while testing. Test data is used for positive and negative testing to verify that functions produce expected results for a given input. It is also used for negative testing to test a software’s ability to handle unusual, exceptional, or unexpected inputs.
Poorly designed test data may not all possible test scenarios, which will hamper the software’s quality. As a tester, you may think that designing test cases is challenging enough, then why bother about something as trivial as test data.
Several techniques to generate the test data, yet every method has benefits and drawbacks. They are:
- Mass copy of data from production to testing environment
- Mass copy of test data from the legacy client system
- Automated generation tools
The primary disadvantages of the above-mentioned techniques are time consumption, lack of data privacy, need for more human effort, etc. To overcome this, creating Synthetic Data Using Deep Learning is the solution we are proposing.
Introduction to Synthetic Data
By 2024, 60% of the data necessary to develop artificial intelligence (AI) and analytics will be produced synthetically due to the field’s rapid advancement. The use of synthetic data will have the volume of real data needed for machine learning. SDG methods have recently become so powerful that the generated dataset are good proxies for the original data and can capture strong and subtle signals.
Benefits & Applications
The synthetic data will not look exactly like the real data because if it did, we would essentially be replicating the original data, which would raise privacy concerns. However, synthetic data has several benefits over real data:
- Overcoming real data usage restrictions: Real data may have usage constraints due to privacy rules and other regulations. Synthetic data can replicate all-important statistical properties of real data without exposing it.
- Creating data to simulate not yet encountered conditions: Where real data does not exist, synthetic data is the only solution.
- Immunity to a few common statistical problems: These can include item nonresponse, skip patterns, and other logical constraints.
- Focuses on relationships: Synthetic data aims to preserve the multivariate relationships between variables instead of specific statics alone.
Application areas of Synthetic Data:
- Automotive & Robotics
- Financial Services
- Information Technology
- Media & Marketing
Infosys Synthetic Data Generation (ISDG) is a platform where users can download a huge amount of test data to test their applications. The requirement of synthetic data will influence what type of algorithm is used to generate data. Here, Generative Adversarial Networks handle the complete process of creating synthetic data (GAN). GAN is an approach to generative modelling using deep learning methods, such as Convolutional Neural Network (CNN). GAN is the combination of two neural network algorithms. One is the generator model, and the second one is the discriminator model. In the generator, the model digests random input from some latent distribution and transforms these data points into some other shape without directly looking at the original data. The discriminator model digests inputs from the original data and the output of the generator, aiming to predict where this input originates.
Features of ISDG
ISDG platform consists of numerous features such as:
ISDG will be capable of generating text, image, audio, and video data.
- To generate synthetic data GAN required some fake input data. One can get the fake data from either the database or a file.
- It will automatically handle the relationship between two or more tables.
- It provides the user to select the PII and Non-PII elements then the generator model will generate according to that.
- It also provides the provision to the user to apply some specific constraints to the element before generating data.
- Users can download the generated data in a different file format such as .xlsx, json, .csv, etc.
- The user can utilize evaluation techniques to assess the quality of the data after it has been generated.
- Finally, the user can do the data cleansing process on the generated data to remove the noise or empty spaces.
With the help of the ISDG platform, users can generate synthetic data in large volumes. Also, this platform will handle most of the existing real-world challenges such as the relationship between tables, data privacy, faster processing, less human intervention, data accuracy, etc. This Artificial Intelligent solution can help alleviate the challenges around data generation in terms of complexity, volume, and need for expertise.