Test data is the data used to test a software application. It is used for positive testing to verify that the function delivers the expected results as directed and for negative testing to test if the software can handle any unexpected or unusual inputs.
Around 15 percent of the total project execution time goes into creating test data. So, an effective test data management in place can facilitate the early start of the testing process and help find quality defects early in the product development lifecycle. A few examples of test data include different sets of usernames, passwords, different file types, different data sets, etc.
Test data is categorized into two types– static data and transactional data.
While static data comprises names, currencies, countries, etc., which are not sensitive, but when it comes to transactional data it involves data like credit/debit card numbers, information on bank accounts or it can even be your medical history or SSN and there is always a security risk of this vital information getting stolen.
Now that you have brief knowledge about test data, here are some critical signs that will help figure out when is the right time to device a test data strategy.
- A test failure in different environments
- Repeated runs giving different results
- The application encompasses multiple systems, and it becomes tough to troubleshoot
- Your team is unable to find the root cause for the defects being raised
A typical test data management would require the following five steps:
1. Exploring the Test Data
Data is always scattered across systems and resides in different formats. Additionally, different rules may be applied to data depending on the type and location.
Test teams should identify their test data requirements based on the test cases— meaning they must capture the end-to-end business process and the associated data for testing. This can be for a single application or multiple applications.
For example, a business may have a CRM system, an inventory management application, and a financial application that are all related and require test data such as different types of accounts, products, mapped inventories, etc.
So, when testing data, concentrate on:
- Missing data
- Duplicated records
- Incorrect data in fields
- Accurate reporting
- New data entries
- Data search functions
2. Subset production data from multiple data sources
The sub-set of production data allows users to ensure realistic, referentially intact test data from a distributed data landscape without any additional costs. One of the best sub-setting approaches includes metadata in the subset to accommodate data model changes quickly and accurately.
In this manner, sub-setting creates realistic test databases small enough to support rapid test runs but large enough to accurately reflect the variety of production data. Part of the sub-setting process involves creating test data to force error and boundary conditions. This even includes inserting rows and editing database tables, along with multi-level undo capabilities.
3. Process and identify sensitive test data and mask it
Masking helps secure sensitive corporate, client, and employee information and supports compliance with federal and industry regulations. The capabilities for de-identifying confidential data must ensure a realistic look and feel and should consistently mask complete business objects, such as customer orders across test systems.
4. Usage of test data generation tools, wherever applicable
Some of the most used test data generation tools are:
- DATPROF for data masking and synthetic test data generation
- EMS Data Generator
- Redgate SQL Data Generator
- Informatica Test Data Management
5. Refreshing data source at regular periods
While testing, the test data often deviates from the required path. But updating or refreshing the test data can help improve testing efficiencies. Refreshing the test data streamlines the testing process and helps maintain a consistent and controllable test environment.
It is required that the test data is refreshed at regular intervals to accommodate functional changes in your application from time to time.
Automate test data result comparisons
Identifying data anomalies and inconsistencies during testing is critical to maintain the overall application quality. The only way to achieve this goal is to deploy an automated capability and compare the baseline test data against results from successive test runs. When these comparisons are automated, they save time and help in identifying problems that sometimes go unnoticed.
The key to implementing any test data strategy effectively is to understand the team’s/projects constraints and align them with the goals for the tests. In the process, it is always advisable to explore alternatives, such as refreshing specific data and generating data for the remaining data fields. One can also check whether mocking the data sources can accelerate testing efforts or not. Managing test data at regular intervals can make the testing efforts accurate, viable, and repeatable.
As testers, we must continuously evolve and apply the most efficient methods for data collection, generation, maintenance, automation, and data management. Contact us and our experts can help you improvise on your test data strategy.
By: Mughni Shareef