Approaches for Test Data Generation in Software Testing

As a tester, your job is not just about testing the software, but also managing, collecting, and maintaining large sets of data. These data sets are crucial for testing all the major test cases to make sure the software meets all the requirements, whether it’s for functional or non-functional testing.

These data sets serve as the input for the test cases, and based on them, the software's output is generated. The behavior of the system is then analyzed to see if it matches the expected results.

We can see in the detail:

What is Test Data Generation?

The Test Data Generation is the process of collecting and managing a large amount of data from various resources just to implement the test cases to ensure the functional soundness of the system under testing. These generated datasets act as the input for the test cases so that the behavior of the system can be checked. Test datasets are designed or selected for both positive testing or negative testing. Generating a rational and relevant dataset is a very complex task because coverage of poorly framed datasets might leave major test cases to be checked. 

Prerequisite: Software Testing | Basics

Test data generation techniques

So, some majorly used techniques are commonly used to Test data generation:

Test data generation techniques - image

1. Manual Test data generation

In this technique, all the datasets are generated manually by the tester with respect to all the required test case through experience and anticipations. 

Pros: Its easy to implement, no additional tools are needed to be deployed and Increase the confidence of the tester.

Cons: Accuracy of data sets generated by this scheme mostly doubtful and also its time-consuming process.

2. Automated Test Data Generation

The major feature of this testing that makes it more efficient than the above technique is the speed, automated data generation technique produces data as in an expedited manner through analyzing large volume of data in a small-time interval. In this scheme, we use automated tools, there are many available in the market.

Pros: The data sets generated by this scheme are highly accurate and Data generation speed is very fast.

Cons: The one demerit of this method is that it is a costlier method to implement and the second one is that these tools take time to understand the system.

3. Back end data injection Approach

This method is done with the help of using SQL queries. Here a tester writes the relevant query and injects it into the database in order to populate the required data sets with respect to the test cases. This is also an easier method which generates a large amount of data in just a few minutes. We can update the database in this scheme if some new datasets are found through other resources like sample XML documents etc. could be updated for future use if required.

Pros: It is less time-consuming technique. Less expertise required as compared to the above technique as you only need to write a correct query to populate data required.

Cons: If you write any invalid query or incorrect it may populate illogical dataset or may cause the failure of your database system so keep attention while injecting any query into database.

4. Third-party tool

A number of tools are available in the market that is processed or provided by the out premises tools. These tools first understand the scenarios of your system under testing and then generates dataset as per the requirement. These tools are customizable as per your need of the business. These tools provide wide coverage and accuracy in generating datasets.

Pros: These tools are accurate because they first understand the entire system and then generated the datasets accordingly.

Cons: Costlier technique to implement because the price of such a tool is high as compared to other technique. Less coverage in case of heterogeneous testing environment because these tools aren't generic in nature.

Conclusion

Test data generation is essential for ensuring that software functions as intended by providing the necessary data for test cases. There are several methods to generate test data, each with its own strengths and weaknesses.

Manual Generation is simple to execute but can be time-consuming and prone to inaccuracies. Automated Generation offers speed and accuracy, though it can be expensive and requires time to set up. Back-end Data Injection uses SQL queries to quickly populate the database, but it requires attention to avoid errors. Third-Party Tools are accurate and customizable, but they come with high costs and may not be suitable for all environments.