Black Box Testing Approaches, Steps, and Benefits
How best to use black box testing and predictive test selection to shorten test suite runtimes.
Key Takeaways
Black box testing, also known as closed box testing, focuses on the inputs and outputs rather than the system's inner workings.
Black box testing is valuable for simulating end-user experiences, creating test cases easily, automating relevant tests, and getting valuable external perspectives.
Teams striving to shorten test suite runtimes can harness predictive test selection for their black box testing use case to run the most important tests based on code and test metadata.
The only way to feel confident about your software's quality and accuracy is to test relentlessly. Software testing ensures an app performs as needed and wanted, and is secure, reliable, and error-free upon release into the world.
Based on your software testing goals, having testers understand the inner workings of a software application can help or hinder your testing. That’s where black box testing comes in. Teams turn to black box testing to find usability, functionality, or feature gaps to give a practical software performance overview.
In this post, we’ll answer all your questions about black box testing, including how to perform black box testing, techniques for black box testing, and simple ways to rev up your black box testing for faster testing velocity.
The basics of box testing: Black, White, and Gray
There are dozens of software testing approaches, but the three most prevalent software testing method categories include black box testing, white box testing, and gray box testing.
Black box testing tests the functionality of an application without exploring the inner architecture. Black box testing is also known as closed box testing because this testing focuses on the inputs and outputs rather than the system's inner workings. When they run test cases, black box testers have zero knowledge of internal structure or the software's source code.
Black box testing improves software quality and reduces the time to release to market. Additionally, it helps teams mitigate the risk of software failures at the user's end because the goal is to engage with the user interface, test functionality, and ensure all inputs and outputs meet the specified requirements.
White box testing tests the internal structures or workings of an app. White box testing verifies the flow of inputs and outputs within a product and can help to improve an app’s usability, design, and security. White box testing is the opposite of black box testing, as it analyzes the internal structure of software, including the code.
If you’re familiar with color theory, it’s likely no surprise that Gray box testing combines black and white box testing. Gray box testing tests internal structure and functionality. Gray box testing helps devs find defects and bugs caused by improper use of functionalities or an incorrect internal structure. Altogether, black box testing, white box testing, and gray box testing all help to release the most reliable, high-quality software app possible. But, black box testing is most helpful for understanding contradictions in functional specifications without interfering with more significant code segments.
The seven standard black box testing steps.
Black box testing has a typical seven-step flow for execution. Based on the type of tests you are using the black box approach for, specific steps may vary but likely follow a similar flow:
Understand the requirements and specifications of your software application.
Identify valid and invalid inputs and their corresponding expected outputs. This ensures the system will detect outcomes correctly.
Create test cases for different test scenarios and inputs.
Run the tests.
After running test cases, verify the expected outcomes against the actual test outcomes.
Fix any errors or issues from the failed test cases.
Finally, retest the application as needed to ensure fixed bugs do not reoccur.
Primary black box testing techniques.
Black box testing is most often used to check if software exhibits a regression, or degradation, from one version compared to a newer version. There are three main types of black box testing techniques:
Boundary value testing - Tests between the extreme ends or boundaries between partitions of the input values.
Decision table testing - Tests system behavior for different input combinations.
Equivalence class testing - Divides the input data of a software unit into sections of comparable data to create test cases.
What types of tests are best for black box testing?
Black box testing is used for functional, non-functional, and regression testing. Black box testing is frequently used for functional testing, which helps validate the system against the functional specifications and requirements. Running black box test cases on specific actions and functions of your app and comparing outcomes to the actual output helps teams uncover flaws and bugs without the need or bias of technical resources.
Black box testing can also be helpful for non-functional testing, assessing the non-functional requirements of an app, meaning the way a system operates. Black box testing for functional tests assesses performance, scalability, and usability. This kind of black box testing helps to reveal any critical issues that can pop up during application use but may not necessarily be related to the app’s overall functionality.
Regression testing can also utilize black box testing, checking if a new software version exhibits a regression (or degradation) from one version to the next. Regression testing helps to assess app functionality continually. Black box testing for regression tests can help DevOps teams avoid issues later in the development process when mistakes and errors cause more headaches and cost more to fix.
Black box testing empowers teams to:
✔️ Simulate end-user experiences.
✔️ Not require highly technical testers.
✔️ Create test cases easily.
✔️ Get valuable external, non-developer insights and perspectives.
✔️ Automate relevant testing.
Automate black box test case selection with machine learning.
If you want to speed up your black box testing cycle times, running the most important tests at the most reasonable time is critical.
Launchable’s Predictive Test Selection machine learning model selects and runs the most critical tests based on code and test metadata with the highest probability of failing. With each change, Launchable ranks tests by importance so that developers can create a unique subset based on the importance of code ranking. With Launchable, you can create a much shorter dynamic subset of longer-running tests and run them on every pull request (pre-merge).
Launchable also empowers teams to track test suite health metrics for better testing intelligence, including:
Flaky test insights find the top flaky tests in a test suite.
Test session duration insights show increases in test session time, revealing upwards trending developer cycle times.
Test session frequency insights tell you which tests with lower quality, higher cycle times, or are infrequently run.
Test session failure ratio insights point out the tests failing most often, which helps determine stability.
If you’re ready to speed up your black box testing times, reduce the size of your test suite, and enjoy a more streamlined workflow, you’re ready to try Launchable.
