Test Automation for Continuous Delivery

If you want to maintain quality of your software as it moves through the CI/CD pipeline, you need a test automation solution that can handle continuous delivery.

Continuous delivery means deploying software as soon as changes are made to code, ensuring that new commits don’t introduce bugs or break existing features. Automated testing ensures that the feedback reaches your team almost instantaneously and helps you find and fix issues early.

Synthetic Test Data Generation

Synthetic test data generation is a powerful capability that enables swift, scalable and secure provisioning of test data. It eliminates the bottleneck of requesting production data from QA teams and removes the need to mask data, both of which can add significant time to a testing project.

Moreover, synthetic test data can be generated at a rate of thousands of rows per second and supports various types of data. This enables testers to provide their own data whenever they need it and discard it once their tests are over.

The ability to generate a wide range of test data for different scenarios is essential to any testing process. These include:

Performance and security testing

During performance testing, data must be realistic to ensure it performs as intended. This requires data that reflects the entire architecture, including connected systems. It also requires data that accurately reflects high levels of load and stress.

Insecurity testing

Security testing is aimed at uncovering risks and vulnerabilities, so data must be accurate to cover authentication, databases and file structures. For this, a high-quality AI-generated synthetic data set is essential.

A truly AI-powered synthetic test data generator retains the data structure, the referential integrity of a sample database and has additional, built-in privacy checks when generating the data. This can be difficult to achieve with a simple table generator.

Versatility

The test data management market is expanding, with an expected CAGR over the next five years. This is because of the need for better and faster data provisioning to support agile software development.

The most effective synthetic test data generators are designed to be easy-to-use, yet also powerful enough to meet the needs of any team. Whether you need to automate a small business or a large enterprise, there is a solution for you.

EU GDPR Test Data

The European Union’s General Data Protection Regulation (GDPR) imposes strict privacy laws on the way organizations handle personal information of EU citizens. Whether a business is based in the EU or not, if it offers goods and services to individuals in the EU, it is required to comply with these regulations.

The GDPR protects a broad range of personal information, from basic identity information (such as name and address) to sensitive PII such as financial and health information. Moreover, it outlines time limits for breach notifications.

While testing a software product, companies must use test data that reflects real-world data and is as similar as possible to that of actual users. This can be a challenge for organizations that already have customer data in their systems, as they may have to manually generate new test data.

To overcome this issue, companies can rely on automated test data management tools that automatically provision new or updated test data to meet the requirements of the GDPR. These tools can help to alleviate the need for manual testing, which can be a major issue in today’s fast-paced testing environments.

Automating the testing process with GDPR-compliant test data is the most effective way to prepare for compliance. These tools can also help with the detection of potential compliance violations.

In addition to the test data management tool, testers should document all of their testing activities. This will give them a clear picture of how they are using their test data and whether it is compliant with the regulations.

Test Data Management

Test data management (TDM) is the process of providing automated tests with the data they need, in the right amounts, formats, and timing. This ensures that the testing process runs smoothly, resulting in more accurate and bug-free software products.

Often, automated tests need to use large quantities of production data. This can be challenging to acquire and manage. Organizations need to develop practical, compliant, and productive processes when preparing production data for automated testing.

This can be done by generating synthetic data on demand, as well as using test data management tools to provision and mask production data. It can also be helpful to have a central repository where all of the different kinds of data required for various types of tests can be stored.

To maximize the efficiency of test data management, organizations should create a comprehensive and detailed TDM plan that details their test data requirements. By clarifying these requirements, companies can make the best use of available resources to create quality test data.

Another advantage of test data management is that it can help minimize the risks of releasing production data into the testing environment. This is because the production data may contain sensitive information that needs to be masked before it can be used in testing.

A properly organized test data management strategy can minimize the number of code copies and keep track of the source codes used for testing. It can also automate a variety of test scenarios and prevent repetitive errors in production by enabling standardization across groups.

Data generation tools

Test automation is an important part of a continuous delivery pipeline. It accelerates software development by automating testing earlier and throughout the software lifecycle. Whether your team uses agile development practices or an established CI/CD cycle, test automation can help reduce errors and defects, improve scalability, and increase your ability to deliver on time.

Using a modern test automation tool is essential to building a quality culture and improving testing coverage. Look for tools that are easy to use, reliable, and integrate with your CI/CD system. The easier a tool is to use, the quicker you can ramp up your test coverage and improve your team’s productivity.

Synthetic data generation is a key element of the testing process and helps ensure realistic results. You need a data generation tool that can provide lifelike, realistic data for all types of tests and enables you to generate large amounts of data quickly.

Data masking is an essential aspect of the data generation process, as it prevents sensitive and confidential data from being exposed. This is especially important in a regulated environment like the financial sector where sharing real customer information can be severely restricted.

A synthetic data generation solution that can maintain relational integrity will make sure that the data it generates is not altered during the test execution. It will also enable testers to verify and validate the results of their tests.