Software Testing - Test Data Generation

Software Testing is not only restricted to test and verify the software quality but it also maintains, gathers, and stores a big chunk of data to cover all the critical test cases, and to confirm if every requirement (both functional, and non-functional ones) are met. The data are fed as inputs to a test case, and after a test case has completed its execution, output data is generated. The analysis of the output data confirms if software is working as expected.

What is Test Data Generation?

The test data generation is the process of gathering, generating, and controlling a large data set from multiple resources for the test cases to check the software functionalities. The data sets basically act as an input to the test case to verify the software behaviors.

Test data generation is done for positive, negative, and edge test cases for a particular requirement. Generation of relevant test data is a critical step, and an irrelevant data generation leads to incomplete test coverage, or missing out a requirement.

Techniques of Test Data Generation

The techniques of test data generation are listed below −

Manual Test Data Generation

In manual test data generation, the data sets are produced by testers manually based on his knowledge on the product, testing skills, and the requirements. The advantages of this approach is that manual test data generation can be adopted very easily, without requiring additional tools.

Besides, testers become more confident with the data they are using to test the product. The disadvantage of this approach is that it takes a lot of time to generate test data manually. Since manual effort is made, also there are chances of human error.

Automated Test Data Generation

In automated test data generation, the data sets are produced with the help of tools. Thus the rate of generation of large chunks of data over a short time is very high. The advantages of this approach is that automated test data generation has a higher level of accuracy, and speed at which large data sets are created.

The disadvantage of this approach is that it is a costly affair, and the test data generation tools need time to understand the application under test before creating the data sets.

Backend Test Data Generation Through Injection

In backend test data generation through injection, the data sets are created using the SQL queries. A valid SQL query is injected into the database to get the required inputs to the test cases. This is a relatively easier technique since it creates large data sets in a short time. Also, the database schemas are updated based on the newer data sets.

The advantages of this approach is that backend test data generation through injection produces data at a very short time requiring not much technical knowledge of the user. The disadvantage of this approach is that usage of a wrong query populates incorrect data sets for the test cases.

Third Party-Tool Test Data Generation

In third party-tool test data generation, out of the premise tools are used to generate data. These tools first understand the application under test, and then create data sets as per the user needs. They can be customized to create varied data in accordance to the business requirements.

The advantages of this approach is that third party-tool test data generation produces accurate test data, and increases the test coverage. The disadvantage of this approach is that it is a costly approach and produces less coverage test data sets for non homogeneous environments as these tools are not generic in their behavior.

Conclusion

This concludes our comprehensive take on the tutorial on Software Testing - Test Data Generation. We’ve started with describing what is test data generation, and what are the techniques of test data generation.

This equips you with in-depth knowledge of the Software Testing - Test Data Generation. It is wise to keep practicing what you’ve learned and exploring others relevant to Software Testing to deepen your understanding and expand your horizons.