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Approaches for Test Data Generation in Software Testing
  • Last Updated : 15 Feb, 2021

Prerequisite: Software Testing | Basics

As a tester, you needn’t only to test the software product but also have to manage, collect, and maintain a large volume of data sets in order to efficiently test all the major test-cases, to ensure that all the requirements are fulfilling as maximum as possible no matter whether it is functional or non-functional testing. These testing data-sets act as input for the test-cases and later on the basis of these data sets output is generated and the behavior of the system is analyzed whether expected or not.

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 dataset might leave major test cases to be checked. 

So, there are some majorly used techniques that are commonly used to generate the datasets:

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



  1. Easy to implement, no additional tools are needed to be deployed.
  2. Increase the confidence of the tester.

Cons:

  1. Accuracy of data sets generated by this scheme mostly doubtful.
  2. 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:

  1. The data sets generated by this scheme are highly accurate.
  2. Data generation speed is very fast.

Cons:

  1. The one demerit of this method is that it is a costlier method to implement.
  2. 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:

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

Cons:

  1. 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:

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

Cons:

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

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