Randomized testing is a software testing technique that involves introducing randomness or unpredictability into the testing process to discover defects or vulnerabilities that might not be easily identified through more structured testing methods. This approach aims to simulate real-world scenarios where users may interact with the software in unexpected ways or encounter unforeseen issues.
How to use it in randomized testing,
Random Input Generation:
Test inputs, such as data values or user actions, are generated randomly.
For example, if testing a form, randomized testing might involve entering random text, numbers, or special characters into input fields.
Random Execution Order:
Test cases are executed in a random order, deviating from the typical sequential or planned order.
This helps identify potential issues related to the sequence of operations within the software.
Random Environment Simulation:
Randomness is introduced into the testing environment to simulate diverse and unpredictable conditions.
This could include varying network conditions, system loads, or other external factors to identify how the software behaves under different circumstances.
Fuzz Testing:
A specific form of randomized testing where invalid or unexpected data is input into the system to uncover vulnerabilities or unexpected behavior.
Fuzz testing involves providing malformed or random input to the application to see how it responds.
Stress Testing with Random Load:
Randomized testing can be applied to stress testing by introducing random loads or spikes in user activity.
This helps evaluate the system’s performance under unexpected conditions, identifying potential bottlenecks or issues related to scalability.
Exploratory Testing:
Testers engage in exploratory testing, exploring the application with minimal predefined test cases.
The emphasis is on discovery and learning, allowing testers to uncover defects organically.
Boundary Testing:
Random values are used near the boundaries of acceptable input ranges to ensure that the software handles extreme values correctly.
This helps identify issues related to input validation and boundary conditions.