The Characteristics and Challenges of Test Data for Maximized Coverage


Quality assurance professionals have shown an increasing interest in the use of synthetic test data for software testing needs. To make sure that they get the most accurate results, quality assurance professionals are using test data automation solutions to design comprehensive synthetic test data. This data consists of all the possible variations, combinations as well as permutations to completely analyze their testing practices for both positive and negative cases. Test data automation enables the testing staff to carry out in-depth testing procedures needed to uncover any defects missed during less exhaustive testing. Depending upon the efficiency of test data automation service, the sophisticated exercise of testing can deliver a variety of volumes and variations of test data in a limited time.

Before proceeding with the testing methods, it is vitally important to understand the characteristics and challenges of test data to avoid any complications in carrying out the procedure.

Characteristics of Test Data

Controlled Data

Controlled test data refers to the patterns, permutations, combinations, and boundaries involved in testing the data for both positive as well as a negative case scenarios.

Realistic Data

As the name suggests, this type of data refers to a synthetic dataset that looks and acts like real-world production data. In this set, customer names should reflect countries or ethnicities. Other data values including dates, credit card information, account numbers and social security number should be added invalid and relevant format.

Accurate Data:

In several testing cases, the data used for testing is required to represent the the accurate value that is central to a certain case. Most of the accurate data cases are observed in healthcare centres where accurate medical procedure codes are necessary to accomplish the desired outcomes.

Secure Data:

Insensitive cases where the privacy of customer’s data is necessary to ensure, the information is replaced with synthetic data to ensure the security of the actual information.

Stateful Data:

Stateful data refers to the cases where synthetic test data observes different states while testing under the given conditions. For example, 4 consecutive login attempts to an account can be ruled such that after first 3 attempts, the account does not allow the 4th attempt.

Scalable Data:

The test data should be capable of scaling to as many rows for efficient testing as possible in real-time situations. The scalable data refers to the efficiency of synthetic data for stress-test under real-time events or situations.

Unique Data:

Unique data refers to the uniqueness or newness of testing accounts used during the whole process. Existing or already used testing accounts cannot be reused.

Test Data Challenges

When designing the test data, here’s a list of the challenges encountered during the process:


Since synthetic the dataset can be generated as patterned data, there are several patterns that can be appropriate for test cases.


Permutations can be generated to test all combinations of data for a given set of values.


When a testing code contains multiple input values, it may lead to combinatorial explosion. This challenge makes it difficult to perform combinatorial testing.


To conduct boundary value analysis, test data is needed to be near the edge case values for a given set of elements.


For the accurate and proper testing of business logic, the synthetic data should conform to the business logic and allow testing for both positive and negative case scenarios.


Formatting of test data can be tricky as it can be as simple as configuring a SQL or as complex as building a whole testing facility.


Generating massive volumes of synthetic test data and effectively managing it under the given set of variations can be a bigger challenge than it seems.