3 Most Practical Techniques Of Test Data Generation

 

In the modern era of technological advancement and data revolutions, applications and software products need to provide quality and high performance. To achieve such standards, a rigorous test is necessary. 

Testing is the process where the functionality of an application is tested based on data availability. Depending on these changes, updates are implemented in the application codebase. 

So, what's test data? Let's find out!

What Is Test Data?

Test data is the documented information or test parameters which is specifically created and utilised to evaluate the functionality of an application feature or software program. 

These testing data-sets function as inputs for the test cases. Later based on these data sets, the output is produced, and the behaviour of the application feature/specific module is analysed whether expected or not. 

Test data can be classified into two types: positive and negative data. 
  • Positive test data: Used for evaluating whether a certain input for a specified function/feature leads to an expected result. 
  • Negative test data: Used for examining a program's/feature's capability to handle unusual and unexpected inputs. 
Now that you understand what test data is, it's time to look at the various ways you can generate test data manually or using enterprise release management tools.

What Is Test Data Generation? 

The Test Data Generation constitutes various processes and workflows for collecting and managing a huge volume of data from several resources for implementing the test cases to ensure the proper functioning and performance of the application under testing. 

These generated dataset collections function as the input for the test cases for evaluating the behaviour of the software system or application feature. 

Since an application's future quality and performance depends on accurate testing modules, generating rational and relevant datasets is crucial. Poorly framed datasets might leave out major test cases to be checked, thus adversely affecting application quality. 

Therefore accurate test data generation becomes an integral part of software release management best practices.

Let's explore the various types of test data generation.

3 Types Of Test Data Generation

Manual Test Data Generation

All the datasets used for testing are manually engineered by the tester based on the required test case. In this data generation technique, the experience and anticipations of the data engineer/tester.

Advantages: 

  • Easy and less expensive to implement since additional tools aren't required.

Disadvantages: 

  • Accuracy isn't guaranteed every time. 
  • Time-consuming.

Automated Test Data Generation

Automated data generation technique uses data analysis and software release management tools to analyse huge data volumes and produce test data at a rapid pace. 

Advantages: 
  • High-accuracy
  • Excellent speed 
Disadvantages: 
  • Costly 
  • Might require additional training 

Back-end Data Injection

Another simple and efficient technique that generates large volumes of data in just a span of minutes. This technique uses SQL queries. The tester writes the required questions and injects them into the database in order to extract the relevant data sets pertaining to the test cases. 

Advantages: 
  • Less expertise is required than automated test data generation 
  • Minimal time-consuming technique. 
Disadvantages: 
  • Requires database backup 
  • Requires domain expertise to understand the dataset flow in the system. 
Not following a systemic approach for developing data while writing and executing test cases can lead to missing some important test cases. Test data management is one of the simplest and streamlined ways to extract correct data at the right place and time.
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