Organizations rely heavily on their technology infrastructure to enable business growth. Software testing management is critical to ensuring that applications used by the workforce perform as intended and won’t break down when employees are in the middle of crucial...
One of the challenges of software production is the QA (quality assurance) process. You want your end users to have the absolute best experience once your software rolls out. To make that happen, you first need to test your software with numerous scenarios.
One way to put your software to the test is to use test data. This data is different from real user data because it’s not representative of any real-life information. However, it is formatted similarly to real user data so that you can use it to run simulations so that your testing teams can make sure everything is running smoothly.
Because test data isn’t the same as real data, it can sometimes be hard to interpret the results and understand if the test data is testing your software adequately. Here’s what you need to know about generating and using test data.
What is test data?
Test data is a collection of data used to test different scenarios during the software development process. What is testing data good for? Using this data during performance tests is a way of providing challenges to your software and discovering whether it performs well under pressure.
For example, you might use test data when doing database testing to ensure that you don’t lose any of your real data in the process and that all data is appropriately stored and displayed to users.
Software developers usually choose test data over actual data to avoid the risk of data getting lost or altered during the software testing process.
How is test data generated?
You can’t use any old data set as test data. If you want your test data to be useful, it needs to be formatted like actual data and accurate enough to be relevant for the test you’re running. Depending on the complexity of the software that you’re trying to test, creating test data can be an arduous process.
There are two major methods of this level of test data generation: synthetic generation and production cloning.
Synthetic generation, also known as test data fabrication, refers to generating fake data exclusively for the sake of testing the software you’re developing. For example, if you were to type up a spreadsheet with fictional names and addresses to use to test out a mail merge, that would be a manual example of synthetic data generation. You can also use data automation services to create data values automatically, rather than completing this process manually. Creating data automatically rather than manually takes less time but comes with unique challenges that you’ll need to account for.
Production cloning, on the other hand, refers to copying data already in production and using that copied data as test data. In the above example, if — instead of making up names and addresses — you used web scraping to copy real names and addresses, that would be a simplified example of production cloning.
Challenges of test data in development
Test data has different challenges depending on the method you used for test data creation.
The biggest challenge to using synthetically generated test data is being certain that the data you use falls within realistic parameters for whatever you’re trying to test. This can be difficult with more complicated applications of data. Ensuring that large data sets make sense can be a time-consuming process.
When you use production cloning, you don’t have to worry about whether your data makes sense, because the data is real data. But because it would be overwhelming to use full data sets and would cost a lot of money, the key to this approach to data generation is knowing how to copy a portion of data without losing the key relationships between data segments.
Another challenge programmers face when using test data is only testing positive scenarios. It’s important to seek out test data for negative scenarios as well so you can see how your software performs when it doesn’t have all the data it needs to run smoothly.
5 tips for better test data management
Test data is a useful tool, but how do you manage test data in a reasonable way?
An ideal test data set can be used for load testing, regression testing, and database testing, all without unraveling in the process. To create this data set, you need enough data to run your tests and discover any issues with your system, but not so much data that it overwhelms the system and slows down the QA process.
Creating these types of ideal data sets can be challenging. Here are five tips for improving your test data management process and creating the best data sets possible for your distinct testing needs.
1. Avoid security issues by keeping sensitive data confidential
One thing to keep in mind about the testing process is that the software you’re testing rarely has the same security measures in place as live software. So when you’re using test data, it’s especially important to keep sensitive data protected.
If, for example, you have a list of payment methods, including credit card numbers, that’s probably not the best data sample to use for production cloning. If the type of data you’re working with is information that should be kept confidential, you either need to alter the data or switch away from production cloning and use synthetically generated data instead.
2. Identify test data prior to testing
You should know what type of data is in your test. Prior to running a test, identify the test data you have and make predictions about how you expect your software to handle test data.
Keep in mind that some test data tests simple use cases, while other data tests more complex cases. Knowing which data you’re running helps you interpret test results and understand how to adjust your software — if needed — to account for those test results.
3. Audit your data and testing environments frequently
As you improve your data testing process and update your software, your data sets need to improve and update accordingly so you can continue testing new things. Data audits help you locate outdated information and clear it from your cache so that you’re always testing the data sets you want to be testing.
Just as you should update your test data, you should also update your testing environments frequently. Auditing your testing environments involves examining the methodology you’re using and identifying the limitations of the tests you’re running.
4. Automate where you can
Automation saves your company time by simplifying your workflow. Determine what to automate by planning your end-to-end data management process, reviewing types of automated testing, and determining what’s going to save you the most time. Then, follow test automation best practices to implement those changes.
In addition to automating test data, you can also automate tests themselves. Brush up on the nuances of test automation to decide what aspects of the testing process would be most beneficial for your company to automate.
5. Plan your testing strategy and what’s needed in advance
One of the keys to using test data is to generate data in different ways. Using a combination of synthetically generated data, web scraped data, and production cloning prevents you from testing the same scenario over and over again.
To make the most of this testing strategy, you need to plan out your tests and make a list of what you need for each software test you run. Planning what you need in advance ensures you have what you need when you need it and prevents issues on testing day. It also helps you generate the right kind of data. For example, if you know that you’re doing a security test, you need a different data set than if you’re doing black box testing. Knowing the purpose of the data you’re generating helps you hone in on a good test data generation strategy.
Partner with a test automation service today
Generating accurate, useful test data can be an arduous process. You have to become an expert in the software you’re testing, including the types of data sets it’s looking for and the types of data sets that will put a strain on the software. Once you understand that information, you need a method for generating data on both ends of the spectrum, from the easiest data for the software to work with all the way through the most challenging data.
Partnering with a test automation service puts the challenges of data generation and the QA process into expert hands. You can feel confident that your QA process is as accurate and helpful as possible when you work with people who generate test data on a regular basis.
Ranorex offers free trials of our test automation services. Learn whether partnering with Ranorex will work for you by signing up for a free trial today.
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