What Is the Issue Using Production Data for Testing

Many firms have a test or QA environment that is linked to a test/QA data source, which is a database that contains test data. Some of these test datasets contain fictitious data created by quality assurance engineers. This fictitious data is created by hand or with self-written scripts. Yes, this appears to be somewhat outdated, however, it still occurs frequently. However, there are certain drawbacks to this method: many production troubles are caused by a lack of real(istic) test data. Because dummy data does not contain every data issue that exists in production, test findings may be inaccurate or even meaningless.

A copy of a production database that has been masked, or encrypted, and subsetted to represent a piece of the database that is important to a test case is known as production test data. A test data management (TDM) system is frequently used to prepare, control, and utilize production test data.?TDM systems are typically accompanied by a highly controlled and centralized test data provisioning process.?

Production data can be used in a variety of ways, but is it a testing solution? If you utilize it for testing, you’ll be sure to find bugs, resulting in just the right number of high-quality tests, right? This remark, on the other hand, is supported by evidence. It’s difficult to disagree, however, if this approach is the only one utilized, the quality of your product will continue to be questioned because this methodology has so many bugs.

1. Challenges of not using Production Data for testing?

The following are some of the Challenges of not using production data:

• You are risking the poor user experience as well as corruption of data if many bugs would seem in production.
• Unit or system tests are not used to validate your business logic.?
• You cannot conduct integration tests, which ensure that large pieces of code interact properly. You couldn’t possibly have thought of enough of them.

2. What is a Tester to Do in such a Situation

Any tester must be proactive in its approach. It’s similar to a chess game. Why wait for your competitor to win before analyzing your defeat when you can win in three or four moves if you’ve already done the same adversary analysis before you started playing.?Stop Testing in Production!!

But how does one go about doing this in testing? By developing synthetic data. Meaning data created with the intention of meeting the requirements of the tests you’re about to perform in order to validate the codebase. That’s a good approach to be proactive, and it’s also a safer approach to test.?Now it’s time for Unit Tests!!

3. What are the Advantages of Using this Approach

• Your data will be validated piece by piece with unit tests.
• There will be numerous difficult scenarios that will undoubtedly test the majority of integration-related problems.
• A large amount of data will be accumulated in order to enhance performance as well as load tests.
• If you did a wonderful job gathering correct Synthetic data, you may be able to perform appropriate functional testing.

This means you won’t be wasting any of the time you used to while the code was in production.

How TestDel Handle production data issues:

The below points emphasize demonstrate that testing using (a copy of) production data is not as simple as it appears. In fact, copying production data to your test and development environments might be extremely dangerous due to the possibility of sensitive data. Storage and database license (costs) can also be a problem. If you make many copies of the production (one for each test team), the size quickly grows out of hand, and the bill swiftly rises.

TestDel?Test Engineers keep the test environment as “in-sync” with production as possible to achieve the greatest possible quality of software. To catch more (ideally all) bugs, TestDel teams replicate whole production data to QA data sources. However, we do keep the following considerations in mind while working with production data:

We check if there is any personally identifiable information in the data If that’s the case, we’ll hide, filter, or delete this information to comply with privacy laws.

We also ensure the test environment is capable of handling such a large amount of data. We also ensure that if the testing team needs a new copy of production, the previous changes are overwritten, ensuring that the testing cycle is not disrupted.