Techniques Of Test Data Generation That Can Change The Testing Game

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Test Data Generation - Introduction

Test data generation is a critical aspect of the software testing life cycle. Tests like Stress Testing, Performance Testing, and Load testing require a huge volume of data to produce proper results. Thus, testers constantly need to look for ways to generate a large amount of data in addition to the ones they are already collecting & maintaining.

Testers must also ensure the test data are valid, credible, and reliable. According to an IBM study, up to 30% of the test failures are directly the result of improper test data. This makes the process of managing and creating test data a labor-intensive and cost-intensive exercise. This could eat up to 30-60% of the resources of testing projects.

Moreover, the recent advances in data privacy laws require testers to exclude Personally Identifiable Information (PII) from test-case projects. This keeps real-time production data out of most testers’ hands. It raises the challenge of generating fictional yet realistic privacy-compliant test data.

So how do testers generate an adequate volume of test data accurately within a short time frame? Some innovative test data generation solutions to this complex situation have come forth, which we will cover in this article.

Test Data Generation Techniques

1. Masked Production Data:

Using live production data may be a tempting shortcut to generate quick test data, but this method is highly vulnerable to breaches and violates existing privacy laws. However, it doesn’t mean you should stay away from production data. By anonymizing and masking GDPR sensitive PII, you can create a quality, highly representative data set quickly.

If you do not have production data readily available, you can quickly try out the following methods to generate a large volume of realistic test data.

2. Automated Test data generation and 3rd-party tools:

Test automation tools such as Selenium allows testers to reach an adequate amount of data by automating data generation. Time constraints are not applicable in this method, and Web APIs will improve the data’s volume and accuracy. Testers, however, need to spend more time defining test cases while using automated testing tools.

In addition to open-source testing frameworks, various 3rd party testing tools have emerged in the market.  to help testers generate quick test data over time. These tools have in-built test scenarios that cover a wide range of use cases. Most of these tools create highly accurate data based on the parameters set, freeing up time for testers to carry out the testing process. These tools are quite expensive, and you need to consider a tradeoff between time and cost.

3. Based on Mathematical models:

The latest iteration in data generation techniques, mathematical modeling, or path selection modeling generates data based on the test data generator’s predefined paths. It uses a mathematical approach to data generation which works in the principle of: for a program X and user flow Y, inject data A, such that A follows the user flow Y.

Some of the popular path data generator models are the Random test data approach, chaining approach, and assertion-oriented approach. To acquire large-scale adoption, path selection model testing shows enormous potential to replace manual testing methods in their entirety.

Final Words on Test Data Generation

Test data generation techniques are constantly evolving, and testers need to be aware of the latest approaches to build quality software. The techniques discussed can tremendously reduce the time to generate reliable test data and make the testing process efficient. If you need help in adopting the latest best testing practices, reach out to us at QAonCloud.

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