What Is Test Data? Types, Benefits, Tips and Examples
Updated February 3, 2023
Test data is a crucial part of the application development process. By testing preliminary data before completing productivity and efficiency tests, designers can better identify coding errors. Understanding test data can help you determine if a product needs additional development or if it's ready to move on to further testing.
In this article, we explain what test data is, explore how to collect it, describe its benefits and offer advice on implementing test data for efficient data collection.
What is test data?
Test data is information that a researcher uses to test how well an application works. It occurs when a researcher collects data to meet the requirements of a test or to determine if an application or product is ready for further testing.
Test data help programmers identify coding errors during the initial stages of a project. This allows them to make changes and submit an application for further testing before its release.
Benefits of test data
Here are a few important benefits of test data:
Offers the ability to identify coding errors: Test data can help researchers identify coding errors quickly before the release of a program. It can also help improve the security of programs.
Provides a foundation for additional testing: Test data provides a foundation to develop further data tests. It first tests the most basic inputs before moving on to the program's purpose.
Identifies redundancy or unnecessary duplication: Test data can help designers find redundancies or unnecessary duplications of code. This can help lighten code and create a more efficient site.
Provides flexibility in managing applications: Collecting test data can give designers flexibility when managing many applications, especially on several platforms.
Ways to collect test data
Here are a few methods of collecting test data:
Automated data is information that comes from an automated process. This method relies on the use of automation tools and allows a researcher to input specific criteria for data collection. Many researchers rely on the collection of automated data using an automated system that goes into back-end databases and sends data to the front-end for researchers to access.
Manual testing collects test data using valid tests, invalid tests and null data, or a value that doesn't currently exist in the database. Manual data also uses a data set based on the performances of inputs, which means it relies on programmers to input commands directly into the system. Researchers usually have more control over the collection of manual data compared to automated data because they can determine the specific data they want to collect.
Related: A Guide to Manual Testing
Depending on the type of test data you need to collect, you may be able to copy the data you need from existing data. This can reduce the time researchers take to collect test data, especially when a designer wants to move from initial testing to other types of testing quickly. This process may also use third-party tools to help the researcher better understand the test scenarios.
Back-end data injection
It's possible to collect test data from a back-end data injection, which collects data from the back of a server. The back end of a system provides researchers with the data they need using a stored database.
This process uses Structured Query Language (SQL), a programming language that collects certain types of data. The biggest benefit of back-end data injection is it can increase data collection speed. It also allows researchers to collect back-dated data, which is data the program's system collects and stores from a previous date.
Slicing refers to a technique where coders copy only a portion of their production data rather than copying all of it. In general, this smaller amount can suffice for generating sufficient test data. Using the slicing method can help optimize your overall performance and reduce testing costs.
Types of test data
Here are a few types of test data:
Blank data: Blank data measures how a program will respond if researchers don't input any data. It also tests the type and frequency of errors and how the program responds to them.
Valid test: A valid test measures how a system responds to valid data. It usually tests the primary purpose of the program.
Invalid test: An invalid test measures unsupported files or commands. It tests how a program responds to invalid inputs, including the message that it supplies to the user.
Boundary conditions: Boundary conditions test multiple combinations of different values and how they display.
Huge test: A huge test measures if data is ready for additional types of testing, such as load and performance testing. It's usually the last step of the test data phase.
Tips for implementing test data
Here are a few tips you can use when implementing test data:
Use all combinations when testing. Try to use every possible combination of both supported and unsupported data to help ensure the test data measures all necessary parameters.
Collect data using positive and negative testing. Positive testing verifies the functions of a program to produce expected results, while negative testing verifies if a program can manage unusual results.
Give yourself ample time to test data. Be sure to give yourself ample time to gather as much test data as you need to help you work more efficiently.
Reevaluate data during each phase. Consider reevaluating test data at each phase of the project to help ensure productivity and accuracy.
Involve multiple team members in the collection. It's often a good idea to involve multiple team members in collecting test data, including the designer.
Use test data prior to data execution. If you conduct text execution before generating all of your test data, it may present challenges to your overall data management. By creating your test data prior to execution, your team can save time and increase its chances of meeting the deadline.
Ensure your data files are uncorrupted. Before you generate your test data, ensure that all of your existing files remain uncorrupted. To reduce the likelihood of data corruption, perform regular backups and communicate clearly with your team members about when each person is working on the files.
Examples of test data
Here are a few examples of different forms of test data:
Performance testing identifies how fast a database can handle test data. The goal of performance testing is to identify bottlenecks, which is when an application slows down significantly and reduces overall productivity. Performance testing also measures things like capacity, reliability and efficiency.
Related: How To Get Testing Fresher Jobs
Security testing is the process of identifying a program's ability to protect data you collect from users. It tests numerous factors, including authentication, authorization, integrity and confidentiality. It may also test where the program stores the information or how it responds to potential threats.
Black-box testing analyzes the functions of a program without access to the code. This allows researchers to measure how the system responds to a variety of situations, including inputting commands of no data, valid data, invalid data and illegal data formatting. You can apply this to different phases of testing, including unit testing and integration testing.
White-box testing measures the internal structure and coding of a program. It may also test the responsiveness of the code, as well as the occurrence of invalid parameters. White-box testing focuses on statement, branch and path coverage.
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