Testing Rasa projects

In this section, we will start by discussing how to validate data and stories. This step is used to find obvious bugs. Later, we will discuss how to evaluate NLU performance and how to read the corresponding reports. Finally, we will introduce the test story format and learn how to use test stories to evaluate the performance of Dialogue management.

Validating data and stories

If developers can quickly detect whether there are errors and where these potential errors are in NLU data and stories, this can help developers greatly improve work efficiency. In Rasa, there is a command for this purpose:

rasa data validate

The preceding command will detect errors in the data and configuration. Common errors include the following:

• Inconsistency of the training data (the same training data appearing in two or more different intents)
• The intents in the training data being inconsistent with the intents in the domain file (fewer or more intents)
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