Common manual testing categories include functional,non-functional, and structural testing.Techniques often used in manual testing are black box, white box, grey box, and exploratory testing. Functional testing verifies that the software functions as intended, while non-functional testing assesses aspects like performance, security, and usability. Structural testing examines the internal code, design, and architecture of the software.
Purpose of functional testing is ensures the software meets the defined functional requirements, focusing on what the software does. Methods of functional testing includes testing specific features, functionalities, and overall system behavior. Testing a login feature, verifying that a button performs the expected action, or checking if a form submits correctly are the examples.
Purpose of Non-Functional Testing is evaluates aspects of the software that are not directly related to its functionality, but are crucial for its quality. Non-Functional Testing includes testing performance, security, usability, and other non-functional requirements.
Testing the speed and responsiveness of a web page, ensuring the application is secure against unauthorized access, or evaluating how user-friendly the interface are the main examples of non functional testing.
Purpose of Structural Testing is focuses on the internal structure and design of the software, often used in conjunction with white box testing.
It involves examining the code, architecture, and data structures to identify potential issues. Testing code paths, validating the efficiency of algorithms, or ensuring data integrity within the system are the examples of Structural Testing.
Boundary Value Analysis (BVA)
Boundary Value Analysis is a black-box testing technique that focuses on testing input values at the boundaries of an input domain, where errors are most likely to occur. It's an extension of equivalence partitioning, where testers target the edges of valid and invalid input ranges, including extreme and invalid values. BVA helps identify defects related to boundary conditions, such as minimum and maximum limits or overflow/underflow situations.
BVA is a black-box technique, meaning testers don't need to know the internal code to design test cases. Boundary values is the extreme or edge values of an input domain. Mainly BVA is two types. Three type BVA and Two type BVA. In Three-point BVA a common approach where three values are tested for each boundary. i,e the boundary value itself, a value just below, and a value just above. In Two-point BVA an alternative approach where only two values are tested. i,e the boundary value and a value in the adjacent partition.
Benefits of BVA are effective defect detection. It helps identify defects related to boundary conditions. Another advantage is focused testing. It reduces the number of test cases while increasing the likelihood of uncovering defects. BVA Improves the software quality: It ensures the system handles extreme and invalid inputs correctly.
Decision table testing
Decision table testing is a black-box software testing technique that uses a table to represent different combinations of inputs (conditions) and their corresponding outputs (actions). It's particularly useful for testing complex business rules or logic where the system's behavior is determined by various input combinations. By systematically listing all possible scenarios and their expected outcomes, decision table testing helps ensure comprehensive test coverage and identifies potential logical errors.
In software testing, it’s important to check how a system behaves with different inputs. A Decision Table is a useful tool for organizing and managing these different combinations. It helps testers figure out how different conditions and actions should work together, making sure all possible scenarios are tested.
Decision tables can be categorized into several types, each designed to handle different levels of complexity in decision-making logic. Depending on the number of conditions, their interdependencies, and the required actions, different types of decision tables can be used to model the decision logic more effectively.
A Limited Decision Table is used when the conditions are simple and independent, typically with two possible values (for example, True/False). It is the most basic form, offering minimal complexity for straightforward decision-making scenarios. Example of limited Decision Table is login system where conditions are “Username Valid” and “Password Correct.”
Extended Decision Table handles more complex scenarios involving multiple conditions with various alternatives and potential interdependencies. This type is used when the decision logic is intricate and there are many possible combinations of conditions. Example of extended Decision Table is loan approval system with conditions like “Income,” “Credit Score,” and “Debt-to-Income Ratio.”
A Condition-Action Table maps conditions directly to actions, making it easier to visualize how each condition triggers a specific outcome. It’s particularly useful for systems where conditions have a clear, direct impact on actions. Example of condition-Action Table is discount system where “Membership” and “Purchase Total” determine the discount.
A Switch Table is used when decisions are based on a single condition’s value. This type simplifies decision-making by focusing on one key condition that determines the outcome. Example of Switch Table is traffic light control system where the action depends on the light color (Red, Yellow, Green).
Rule-Based Decision Table integrates multiple decision rules to handle complex conditions and actions. It is suitable for systems with several interacting conditions and rules that dictate outcomes.
Example of rule-Based Decision Table is insurance eligibility system based on “Age,” “Driving Record,” and “Location.”
Decision tables bring several important benefits to software testing, especially when you need to manage complex logic. Main advantages are
Clear Representation of Logic: One of the biggest advantages of decision tables is that they make complex rules easy to understand. By laying out the conditions and possible actions in a simple, visual format, they help everyone—developers, testers, and stakeholders—see how different inputs lead to specific outputs. This makes it much easier to spot errors in the decision logic.
Thorough Test Coverage: Decision tables are great for ensuring that all scenarios are tested. They help you list every possible combination of conditions, which means you can create test cases that cover all possible outcomes. This reduces the chances of missing edge cases or scenarios that might break the system.
Streamlining Complex Scenarios: When multiple conditions are in play, managing all the combinations manually can get tricky. Decision tables simplify this by organizing all possible input combinations and showing the corresponding actions. This reduces complexity and helps testers easily design comprehensive test cases without overlooking any condition.
Automation-Friendly: Since decision tables follow a clear structure, they’re easy to translate into automated tests. With automated testing, you can quickly check whether the system behaves as expected across all conditions. This can save a lot of time, especially for large-scale projects, and ensures consistency in testing.
Better Communication Across Teams: Having a decision table is like having a shared map for everyone involved in the project. It clearly outlines how the system is supposed to behave, which helps ensure that developers, testers, and business teams are all aligned.
The future of manual testing in the age of AI
The world of software testing is rapidly changing, driven by the rise of AI and automation. But does this mean the end of manual testing? Absolutely not. Instead, it signals a transformation. The future isn't a battle between humans and machines, but a collaboration. Manual testers are adapting the changes, combining their unique human insights with the power of AI.
Lets think about an AI-powered image recognition app. Automated tests can confirm if it identifies objects, but can they can't tell how it feels? There we are. They evaluate the user experience- Is the interface intuitive? Are the labels accurate in complex situations? Can it handle unexpected inputs gracefully? For example, a tester might discover the app confuses a "small dog" with a "cat" in dim lighting—a subtle error automated tests might miss.
Manual testers will specialize in areas where AI falls short-exploratory testing, usability, and those tricky edge cases. They'll validate AI-generated test cases, ensuring they reflect real-world user behavior. And they'll provide essential feedback on the "human" aspects of software—accessibility, cultural nuances, and overall user satisfaction.
To thrive in this new landscape, manual testers need to expand their skill sets. Understanding AI concepts and learning to work with AI-powered tools is crucial. They'll become experts in recognizing AI's limitations, using their judgment to bridge the gaps. By embracing this evolution, manual testers will remain indispensable in ensuring software quality, bringing the essential human touch to an increasingly automated world.