Test data comes in many formats and volumes, and finding the correct data for production testing can be time-consuming. It’s thought that up to 75% of the testing process can be wasted during the testing phase while waiting or searching for the correct data.
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Therefore, test data management is critical for those wishing to streamline the process and reduce redundancy in application testing phases. But what do we define as test data, and how is it typically managed? Here’s a simple overview of the process.
What is test data?
Simply put, test data is any data used for testing. For example, test data will need to be entered to analyze its output and compare it with the known output when testing a system. So, in this case, even the expected output is a form of test data.
For example, testing the functionality search function on a website, a search term may be entered, and the page may load the corresponding term. In this instance, the search term and the web page loaded from it are both examples of test data.
At the early stages of development, a developer wants to know that every line of code and branch of the program has been checked at least once. Using tests with as little redundancy as possible and keeping a more reasonable execution time.
These tests cover the development process from just before development to near-production.
Approaching production and onwards, the quality of necessary testing data will be different. This is because mass data will need to be used; the more program versions arrive, the more test data is required.
System testing needs to involve valid, invalid, absent, or ‘extreme’ data so that it’s possible to determine how the system functions with each case.
So, the demands placed on test data and the methods used to provide it can differ depending on the point along the development chain. These dynamic requirements mean that it’s impossible to have a single, optimal test data provision method that fits the entire process.
Test Data Uses
Testing a system involves making sure it’s working correctly by entering input test data into the system. This can include entering data that will guarantee the system fails to see how the system responds to errors. Data tests can be of several types,
depending on their desired outcome:
- Simulated data testing uses valid data expected to be entered into the system. These data should typically be in the same format as the data used by the system. This test looks for unexpected and expected responses to entered data. For example, will it error or crash if the wrong data is entered?
- Live data testing involves entering real-world data provided by participants to ensure the system is capable of doing what it has been designed to do in real time. This is often done using beta versions, which allow the system to be tested online while in use by multiple people entering unpredictable data.
- Volume Data testing and load testing involve checking the capacity of the system to handle data. This might be checking how many people can run the system simultaneously or simply throwing as much data as possible into the system to see how it responds. Does it slow down, does it crash, or can it process the data effectively?
Testing data in Whitebox tests – where the testers have access to the internal design of the application – will typically require different data sets than Blackbox testing – where the participants don’t know about the backend.
So, with the various forms of test data, especially in complex and secure systems, it’s essential to guarantee the quality of your input data to ensure sufficient test coverage. This can be done by Test data management.
What is Test Data Management?
Test data management (TDM) is essentially a way of improving the QA process in software testing, particularly in agile development. The test data management team or software solution helps free testers to work on what they need to do by providing a structure for tracking test data requirements. The goal of a TDM team or software is to deliver test data on goal and budget.
When the application design phase is completed, good test data management provides the required test data design. This means no time is spent looking for or designing test data at the QA sprint.
TDM can be executed through a well-defined staff team or as software solutions. Usually, it will require a combination of both. Software solutions are suitable for automating processes, creating rich, synthetic data where necessary, and calculating essential coverage.
- Efficiency – Up to 75% of the tester’s time can be lost during the testing phase if they’re busy looking for or waiting for test data. TDM cuts out this delay by having the data ready in time for the start of the test phase.
- Reduce Redundancy – By implementing test case optimization practices, TDM helps remove redundancy in test data; this reduces the number of tests that we have to execute and cuts test cycles by over 30%, reducing storage costs by using correct and complete data sets.
- Data masking – TDM strives to eliminate access to personal identification information (PII) data by obfuscating it before it reaches the testing team, reducing the damage of potential data breaches.
Ultimately, a good TDM team or software improves coverage, efficiency, and the skills of the people involved, while increasing the quality and value of the application.
Test Data Management Challenges
Automation can be great at saving time – when it works. But unfortunately, it can take a long time to find where the failure came from and specifically which data were responsible when it failed. In addition, other common issues arise when introducing a new test that inadvertently pollutes another test. So, maintenance and fixing tests are two issues that occur with test data automation.
Masking information can affect testing when testing requires that exact information be effective. For example, obfuscating address information when testing software for a delivery company might make it hard to know if the system is sending items to the right people.
Masking is also not easy to do successfully – For example, even with different personal details (e.g., switching names), individuals can still be easily identified by over-curious devs if they’re the only person within a specific zip code. Being able to track people down from one piece of information is still very feasible. This means developers potentially have access to information such as location, account details, or medical history, all of which are legally and ethically inappropriate.
Test Data Management Best Practices
- Finding the appropriate test data from data stored in different formats,
locations, and types.
- Extracting an appropriate subset of data from these different sources.
- Effectively masking a client’s data.
- Accuracy testing against baseline test data
- Refreshing test data to increase efficiency.
- Automate as much as possible.
These test data management best practices work as an outline for successful TDM. Of course, each step has its challenges and advantages. Still, all must be considered equally necessary for maximizing efficiency in the TDM process and ultimately leading to smoother QA-based testing sprints.
TDM is a handy tool when used correctly. Ideally, good test data management will locate (or create), organize, store, mask, and copy real and synthetic data depending on the task at hand.