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One of my favorite sayings is, “work smarter, not harder.” Basically, it means finding ways to do things more efficiently. This applies to software testing too. It means finding a way to do the least amount of work while still finding the most important problems.
So, let’s talk about black box testing. This is a way of testing software where the tester doesn’t look at the internal workings of the program. Instead, they only focus on what the program does from the outside. They check if the inputs and outputs are working correctly, based on what the program is supposed to do.
It’s called “black box” testing because the tester can’t see inside the box (the program’s code). They’re only looking at what comes in and what goes out. This type of testing is important because it helps make sure the software behaves the way it’s supposed to, without getting into the nitty-gritty details of how it’s made.
Remember, black box testing doesn’t check the insides of the code. That’s where white box testing comes in. White box testing looks at the internal code to make sure it’s working properly. Using both black box and white box testing together helps ensure the software is thoroughly tested and as free from problems as possible.
Here are five types of black box testing techniques we use to improve test coverage while reducing the number of test cases needed. By pinpointing the right test data, we can create a small number of tests that cover a lot.
These techniques apply to all testing levels, from unit testing to system tests, covering both functional and non-functional aspects.
Equivalence partitioning is a method in software testing where we group possible inputs into sets called equivalence classes. The idea is to test just one value from each class because they’re expected to produce the same output. This strategy cuts down on the number of tests needed while still covering a wide range of scenarios. We define these classes based on requirements like data types, ranges, and relationships between inputs.
When setting up equivalence classes, it’s important to include invalid inputs as well. In my experience, most bugs are found with invalid inputs rather than valid ones. Here’s an example:
Imagine you have a field that only accepts integer values between 1 and 10. The equivalence classes would be:
So you’d only need four test cases, one for each partition. Testing multiple values within the same partition doesn’t give us extra insight since they’re expected to behave the same way. For instance, if you tested value 5, you’d expect similar results for values 4, 8, and so on.
Boundary Value Analysis is a technique we use in software testing to look closely at the input values right at the edge or “boundary” of what the program can handle. We do this because these boundary values often trigger errors or unexpected behavior. This happens because they’re special cases or edge cases that the program might not handle correctly.
For instance, if a program accepts integers from 1 to 100, the boundary values would be 1, 100, and values just outside that range like 0 and 101.
There are two types of boundary testing:
Boundary analysis is crucial because it helps uncover errors and ensures the program works well for all inputs, not just the ones in the middle. This way, we can catch and fix bugs that might otherwise slip through unnoticed.
Decision Table Testing is a method we use to test software when there are many conditions involved. It helps us understand how the software behaves based on different combinations of inputs. We represent these relationships in a table format. Each column represents a condition, and each row represents a combination of conditions. For each row, we create a specific test case and include the expected output.
Let’s take an example: Imagine we have a requirement for an app that calculates the cost of a purchase based on the item, quantity, and shipping method.
In this case, we have inputs like Item, Quantity, and Shipping Method, and the output is the Cost. The decision table shows all possible combinations of inputs and their corresponding outputs. This makes it easy to identify test cases and their expected results.
Decision table testing is handy when a program has many inputs and conditions that interact in complex ways. By breaking down these inputs and conditions into a table, it’s simpler to test and verify all the possible combinations and variations.
Pairwise Testing is a clever method used in black box testing to come up with test cases covering all possible pairs of input combinations for a given set of parameters. It’s particularly handy when there are many inputs, making it impractical to test every single combination.
Let’s imagine an app with 3 fields: A, B, and C. Each field can have one of three values: 1, 2, or 3.
Normally, testing all possible combinations would mean testing 27 (3^3) scenarios individually, which is quite time-consuming.
With pairwise testing, we identify test cases covering all unique pairs of inputs. So instead of testing all 27 combinations, we only need 9 tests to cover everything.
Here’s an example:
As you can see, we cover all possible pairs of input combinations with just 9 tests, rather than 27.
There are various tools available for pairwise testing, such as AllPairs, PICT (Pairwise Independent Combinatorial Testing), SmartBear TestComplete, and Pairwise Test Case Generator, among others. These tools help automate the process of generating pairwise test cases, making testing more efficient.
You can also use Excel or OpenOffice Calc with macros or plugins to generate pairwise test cases if needed.
Explore these options to find the tool that best fits your requirements and make your testing process smoother.
Black box testing can be applied to three main types of tests: functional, non-functional, and regression testing.
This type of testing checks specific functions or features of the software. For example, it verifies if users can log in using correct credentials and cannot log in using incorrect ones. Functional testing can focus on critical aspects of the software (like smoke testing), integration between components (integration testing), or the entire system (system testing).
Black box testing also examines aspects of the software beyond its features and functions. Non-functional tests evaluate how the software performs certain actions rather than if it can perform them. These tests can determine if the software is user-friendly, performs well under various loads, is compatible with different devices and browsers, and is secure against common threats.
This form of testing checks whether a new version of the software maintains or degrades its capabilities compared to the previous version. It can involve testing both functional aspects (like checking if a feature still works as expected) and non-functional aspects (such as verifying if the performance remains consistent). Regression testing ensures that changes made to the software do not inadvertently introduce new issues or impact existing functionality.
Many practitioners combine black box testing with white box testing. White box testing involves testing an application with detailed inside information of its source code, architecture, and configuration. It can expose issues like security vulnerabilities, broken paths, or data flow issues, which black box testing cannot comprehensively test or test at all.
By combining black box and white box testing, testers can achieve a comprehensive “inside-out” inspection of a software application and increase coverage of quality and security issues.
While white box testing assumes the tester has complete knowledge, and black box testing relies on the user’s perspective with no code insight, grey box testing is a compromise. It tests applications and environments with partial knowledge of internal workings. Grey box testing is commonly used for penetration testing, end-to-end system testing, and integration testing.
You can perform grey box testing using Interactive Security Testing (IAST) tools. IAST tools combine DAST and Static Application Security Testing (SAST), used in white box testing to evaluate static code. IAST tools enable you to combine the work of testers and developers and increase test coverage efficiently. For example, you can perform more directed tests which focus on areas or user paths that are most likely to contain flaws.
By combining these two testing methods, you can ensure that tests:
Apply knowledge of application structure to identify vulnerabilities and bugs
Evaluate the application objectively and uncover UI/UX issues, as a real user would
Cover all aspects of an application’s functionality
Incorporating these techniques into your testing strategy is a smart way to ensure thorough coverage at every stage of the software development life cycle. To enhance your testing process further, consider integrating additional types such as exploratory or error-guessing testing, compatibility testing, and usability testing.
Moreover, if the test cases resulting from these techniques become part of your regression testing suite, it’s worth considering automation. For UI testing, tools like Selenium, Cypress, and Appium can be valuable, while for API testing, integration tests written by the development team or tools like Postman can be utilized. Automation can streamline repetitive testing tasks, saving time and effort in the long run.
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Black Box Testing Techniques refer to methods used to assess software functionality without delving into its internal code. This approach focuses solely on the inputs and outputs of the software, ensuring comprehensive testing without needing intricate knowledge of its underlying structure.
Black Box Testing Techniques are crucial for software development as they offer a user-centric perspective, mimicking real-world scenarios. By testing from an external viewpoint, these techniques help identify potential issues, ensuring the software meets user expectations and performs optimally.
Black Box Testing Techniques offer several advantages, including thorough test coverage, ease of execution by non-technical testers, and the ability to simulate user interactions accurately. Additionally, they help detect defects early in the development cycle, leading to improved software quality and customer satisfaction.
Depending on the project requirements, various Black Box Testing Techniques can be employed. Equivalence Partitioning, Boundary Value Analysis, Decision Table Testing, Pairwise Testing, and State Transition Testing are some commonly used techniques, each offering unique advantages in different testing scenarios.
Implementing Black Box Testing Techniques effectively involves understanding the software’s functional requirements, identifying relevant test cases, and executing them systematically. Additionally, leveraging automation tools like Selenium or Postman can streamline the testing process, enhancing efficiency and accuracy.
Yes, Black Box Testing Techniques are well-suited for Agile development methodologies. Their flexibility and focus on user experience make them ideal for iterative development cycles, allowing teams to continuously test and refine software functionality throughout the development process.
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