The framework for testing an AI chatbot has three independent layers, which ensures changes do not interfere with the overall system functionality:
- Communication Layer manages data exchange between users and the system, using tools like Jupyter Notebook or APIs
- AI Layer hosts different AI models for tasks like rephrasing questions and validating answers
- Validation Layer performs data validations, ensuring accurate responses and generating reports for QA
Technical Specs:
- Language: Python
- Testing Framework: Pytest
- Reporting Tool: Allure
- AI Models: Various AI models are used for question generation, emotional tone analysis and semantic similarity.
- Deployment: The framework is built for seamless integration into your existing development workflows. It can be easily adapted to work with APIs, CI/CD pipelines or other deployment methods to automate testing in your development environment.
Who Should Use This:
- Organizations developing and implementing AI chatbots
- QA teams responsible for the quality and performance of chatbots
- Product owners and stakeholders optimizing the chatbot user experience
Use Cases:
- Evaluating chatbot accuracy and response relevance across diverse user inputs
- Identifying and fixing bugs or inconsistencies in chatbot responses
- Ensuring chatbot responses maintain a positive or neutral emotional tone
- Enhancing user experience by refining chatbot answers and conversational flow
- Automating the testing process to save time and resources
- Integrating chatbot testing into your CI/CD pipeline for continuous quality assurance
