If you are working on an AI application and using defect-tracking programs to make it perfect, you should also know about metamorphic testing. It’s always challenging to deal with AI-based app testing, especially considering its complexity. AI systems are self-learning and tend to change the output, making it almost impossible to set up a proper test case. Still, you cannot afford to leave the system without a test, so here’s what you can do to test your AI applications and make them as stable as possible.
Metamorphic Testing – What Is It?
Similar to bug tracking software, metamorphic testing is meant to improve your product. This technique, which is property-based, uses the AI system’s generic relations to describe the functionality instead of the input and output relation. Afterward, it explains how an input change will become evident in the output.
MT is different from traditional testing. The technique focuses on an input transformation and then analyzes whether the metamorphic output satisfied the relation. A system failure occurs when the relation is violated.
The metamorphic relations that are the most used in this type of testing are increase, decrease, and invariance. Here’s what they all mean:
- Increase – The output should increase when the input shifts in the same way someone is more likely to go bankrupt when they have no house.
- Decrease – The output should decrease when the input shifts, such as the probability of someone going bankrupt when they own many properties.
- Invariance – The output should remain the same when the input is changed with a synonym.
Standard Terms Used in Metamorphic Testing
You’ll come across various new terms when working with test management software and metamorphic testing. Here are some of the most common ones:
- Transform – This refers to the change of a seed input in order to get a new input.
- Seed Input – It represents the test’s first seed input and the one that will be used for the transformation process.
- Metamorphic relation – There will be a known impact on the output when the input is being transformed.
Examples of Metamorphic Testing
Here are some examples of this testing type in AI applications:
Recognizing Objects
When you have two photos taken at the same place but at different times, there will be differences, especially if one is taken during winter and another during summer, or one during daytime and the other during nighttime. The system should be able to categorize the object correctly.
Categorical Perturbation
MT also works in cases of categorical perturbation. For instance, someone who has a part-time job and not a full-time one will experience perturbation. Therefore, the score will increase, representing the metamorphic relation. The default probability can be tested for the increase following the perturbation.
Final Thoughts
Metamorphic testing comes in handy when you want to improve your application. The practice helps describe the functionality of the relations of an AI system. It also helps see how input transformations reflect in the output. The method can be used in cases of categorical perturbation, object recognition, and more. Overall, it works hand in hand with your bug tracking software and makes your products more reliable.
