Data is essential for many businesses as data is becoming very crucial in an increasingly digitized world. Many people are still not aware of the data role in the role of testing. The perfect option for the best way to test your application is real data. Use test data instead of using the production of data directly. This refers to the term to the generation of data, which comes as close as possible to the production data without revealing any sensitive information related to the business.
Test Data Management
Test data management is the formation of non-production data sets that accurately mimic organizations’ actual data. The systems and the application developer can operate rigorous and valid system tests. According to the State of DevOps 2019 report by Redgate, 65% companies copy their production data in order to be utilised in testing. Well, you will be surprised to know that only 36% of these companies apply masking techniques in order to protect their data from hackers.
The quality of test data matters, and if the applications are tested against generic data can cause several issues when that application is put into production. To avoid these issues, applications must be tested brutally against data similar to the actual data.
Production of data is often not practical for a test system because of security and regulatory concerns. Data that contains personally identifiable information must be altered to protect people from gaining sensitive data exposed to the development and testing terms. Test data management uses data masking techniques to blur personally identifiable information.
Who uses Test Data Management?
Test data management is mainly used to reorganize a lot of critical business processes, including a lot of sensitive data information. It is essential for industries like healthcare where a breach of keen clients’ data could be extremely damaging. Most of the organizations contain some sensitive data and need to be asked for testing.
Benefits of Test Data Management
Test data management helps the organization make better quality software, enabling us to perform reliably on deployment. The test data management system prevents bug fixes and rollbacks the overall creation in a cost-efficient software deployment process. It also lowers the security risks.
What is the use of the accuracy of test data?
It actually doesn’t make sense to test the software at all. Meaningless data. It is probably better not to perform testing with such data as it may seem like pushing nothing. Empty data is worthy of doing the test as it does not add any value to the applications’ quality. Test data needs to be somehow meaningful to perform valuable test data without revealing or exposing any sensitive or private information. Additionally, continuous testing has gained a lot of attention within the DevOps and software development companies.
What are the key pointers to ensure the effective maintenance of the test environment?
As test environments, most of the time, it contains heterogeneous platforms and stacks, which presents the below crucial pointers to ensure the test environment’s effective maintenance.
- Effective environment distribution and sharing
One of the critical challenges of test environment preparation is that many teams or people need to use the same set of testing resources. It is a suitable sharing mechanism that needs to be developed that caters to all groups and people without delaying their schedules.
This can be achieved by the maintenance of a repository or information link wherein all the data regarding-
- Who is using that environment
- When the environment is free
- How the distribution of environment usage time and how it is entered accurately
- Where the required of the resource is large and limited availability
The second aspect is to revisit the terms resource required for each testing cycle and look for which resources are not utilized heavily. By analyzing all those specific resources can be replaced with any new resources or systems.
The test requires some needy comprehensive test setup or setup involves detailed steps that are exceptionally time-to consume. This is the case during the end to end testing, which involves two or more components to work together. You can use a similar condition to be reused by multiple terms. In this case, a good understanding of the entire environment is fundamental to collate what kind of tests are being performed by the various teams. This will paint a good picture that provides the specific resources to the respective terms.
When new functions and features are being developed, regression tests need to be performed for these functions for every release cycle. The test regression seems to be running on the same test setup with the same data. In actuality, they always involve every release following the features of implementing.
Every product release cycle has one or more rounds of regression testing to establish the regression test environments for every product release cycle and reuse them within the test cycle, which is used to portray the test environment’s stability overall. Developing automation frameworks also improves the efficiency of the test environment as automation assumes that the test environment is stable, and the defects are purely code-oriented.
How to use Test Data?
- At very first, make sure to make a copy of your test data before using it. This step is very crucial, and one must follow this step. This will help you when something goes wrong. You will be able to access the original test data set very quickly. If you don’t make a copy, then it may cause severe damage.
- Now, you can use the test data in several ways. Scripts of data can be tested in different formats or insert the data into a database. Directly inject the data into a test database to test whether the application runs accurately or not.
- After proceeding with the test cases, you can perform several things with your data, like you can store the final state of the database as a reference. You can delete all test data to avoid confusion between tested data and the original data file. You can clean the imported or outputted test files in your applications. However, it can be very tricky to clean accordingly. But you can do this to avoid any further issues.
- It is very challenging to get all data aspects in the right form.
- After testing data, this test data will save your organization a lot of time, and this approach only covers the happy path.
How to generate Test Data?
To operate quality testing, you will need data of too high quality. The main motive of the test data generation is to generate meaningful and interrelated data.
According to the state of DevOps report of 20q9 by Redgate, 65 percent of companies copy their production data and are used in testing. It is a very worrying issue that only 36 percent of the companies apply masking techniques to protect sensitive information from hackers. Basing the test data on production data is not a wrong approach. Production data resembles an accurate picture of the data, which works perfectly with the application. This ensures that the data is well suited for running test cases. It is crucial to mask or substitute sensitive data to avoid disclosing any personally identifiable information, which may further damage severe damage.
Three Common Pitfalls When using Production as Test Data
Using the production data might become a smart approach for your organization for generating test data in the most convenient possible way. But many organizations forget about the limitation of the test data. Here is some common pitfalls companies encounter when basing their test data on production data.
Pitfall #1: Missing Data
When the development teams make new functionality, this might introduce your organization to the original data captured, which means that you will have modern tables in your database. For that, you don’t have any simple data. When you perform blindly copying of production data for using it as test data, you might forget about the new database tables. It is essential to analyze any new data introduced that you are testing engineers need to generate.
Pitfall #2: Production Data Which Follows the Happy Path
A happy path is a common word for testing production data to define testing data’s success scenario. This path is also very straightforward to find in production data as every action should be successful, which users completed. By considering this, the production data will not be the ideal data set to use for testing, and more likely, you will have to make data for adverse scenarios. This will help you to secure your misfortune when test failures.
Pitfall #3: Testing Edge Cases
The production data often does not represent any edge cases due to the production data representing the happy path. You will not be able to find many edge cases or any advanced flows in your data. There might be some hindrance if you want to test all possible data scenarios to gain accurate test coverage. You will not be able to reach precise test coverage solely with the production data. Your application will require additional data to show advanced flows, and generating data might require a manual approach more.
Strategies for Test data management:
- Analysis of data
Test data is generally constructed based on the test cases to be executed, like in a system testing team with end-to-end test scenarios that are needed to identify based on which test data is designed. This includes more applications to work. This involves the management controller application and the database applications to function in correlation with one another. Data analysis thoroughly is all different kinds of data that may be required to be made to ensure effective management systems.
2. Data setup
This process is an extension from the previous steps, enabling us to understand what the end-user or production scenario will be and which data is required for the same. One can use that data to compare that data with the information which currently exists in the current test environment. We need to set up data to mirror the production environment, and based on this; new data may need to be made or remodified.
3. Determination of the Test data
Depending on the testing, the test data may need to be altered or created. This test data is not immediately relevant but may be required at a later point. A transparent process of deeming when the test data can be cleaned up should be formulated.
4. Inspect the sensitive data to protect it
There is a large amount of sensitive data required to test applications many times properly. A cloud-based test environment is a trendy choice as it renders on-demand testing of different products. Something is as essential as guaranteeing users privacy in a cloud, which is a cause of concern. Specifically, when we need to replicate the user environment, the process of shielding sensitive data must be identified. The mechanism is mainly governed by the volume of the test data, which is used.
5. Automation
As we adopt automation to run repetitive tests or for running the same tests with different kinds of data, it is possible to automate the creation of test data. This procedure will help in exposing any errors which may occur concerning data during testing. Comparing the results produced by a set of data from the consecutive test can be a possible way to do this, and then the automated term is compared.
6. Useful data refresh with the use of central repository
This is the most crucial methodologies which form the heart of implementing data management. As mentioned earlier, all the points, specifically those concerning data setup and data clean up, are directly or indirectly correlated. Using a lot of effort in creating test data can be saved by the maintenance of a central repository, which combines all types of data required for several kinds of testing. This buyer’s guide can help you evaluate automated testing solutions.
Conclusion
In every test, the test environment should give prime importance. Every new cycle will come up with all new challenges and combat with an unreliable and not planned test. Many organizations put their strategies in place, like forming dedicated test environment maintenance teams as an extreme measure and establishing specific frameworks for their adequate test maintenance. This ensures smoother release cycles. So, you must hire Amplework to do testing data management.
