By Jennifer Castro, Principal QA Automation Engineer at Growth Acceleration Partners and Juan José Barboza, Staff QA Automation Engineer at Growth Acceleration Partners.
With businesses continuing to seek more efficient ways to meet their customers’ needs, there is no doubt chatbots have gained traction when it comes to delivering customer service.
Nonetheless, several challenges come into play when implementing such AI-driven applications that require comprehensive testing. Unfortunately, standard automation solutions do not prove to be sufficient enough when faced with the complexities encapsulated with chatbot testing.
In this post, we explore how Growth Acceleration Partners (GAP) has seamlessly integrated Artificial Intelligence (AI) with automation to develop a robust and efficient testing framework for our internal chatbot. We analyze the AI application we tested, discussing the challenges it presents. Additionally, we detail the testing structure we devised, highlight the key AI concepts used, and examine how these concepts were applied in practice.
The Challenges of Testing AI-Powered Chatbots
Testing chatbots though, is different from testing traditional applications. Addressing the non-deterministic nature of AI is crucial. Even when presented with the same input, an AI chatbot can produce different outputs due to its ability to learn and adapt. Because of this, it is impossible to rely solely on static validation techniques.
In addition, language use and different phrasing or communication styles also vary greatly and make testing quite complex. A vital concern remains, establishing that the AI can comprehend a sufficient number of queries in order to respond correctly to the user.
Key AI Concepts for Effective Testing
To address these challenges, GAP incorporated a few prominent AI concepts:
- Natural Language Processing (NLP): It allows computers to understand and produce human speech. From a testing perspective, NLP is essential for processing and analyzing the questions and answers in chatbot interactions, ensuring the chatbot responds accurately and effectively.
- Semantic Similarity: It measures how similar two pieces of text are in meaning, allowing more flexible validation.
- Sentiment Analysis: It determines the tone of the text, ensuring the chatbot’s responses are appropriate and consistent.
- Generative AI: It generates new content, such as rephrased questions, to simulate real-world scenarios.
- Discriminative AI: This type of AI is highly effective for classifying information. In the context of chatbots, discriminative AI is utilized to sort responses based on various criteria, ensuring the replies are pertinent and on-target.
Integrating AI and Automation
This framework uses a traditional software testing methodology known as Data-Driven Testing (DDT), which involves managing the test inputs and outputs through an external data source, avoiding hard-coded values in the test code. By leveraging DDT, it’s easy to add new inputs to the data source as new scenarios are introduced into the system under test.
The testing framework implemented by GAP applies DDT by storing questions, expected answers, keywords and style information in a CSV file, which acts as the data source. This allows the seamless addition of new question information without needing to modify the testing code.
Generative AI is used to rephrase questions in different styles, mimicking real-world user interactions. Semantic similarity and sentiment analysis are applied to validate the chatbot’s responses, ensuring they are accurate, relevant and appropriately phrased.
Framework Architecture and Implementation
The framework consists of several elements:
- Data Layer: It contains test cases and test data stored in a CSV file.
- AI Layer: It processes questions, generates new variations and performs semantic analysis and sentiment analysis.
- Communication Layer: It communicates with the chatbot through API.
- Validation Layer: Monitors the responses provided by the chatbot to determine if they are as expected.
- Reporting Layer: It prepares reports including details and analysis of test results.
The framework is developed with Python language using pytest framework and Allure for reporting. The framework integrates with a CI/CD pipeline for automated testing and continuous feedback.
Conclusion
Because of the effective combination of AI and automation, GAP could develop a robust testing framework that has the ability to tackle challenges associated with chatbot testing. This approach ensures chatbots deliver accurate, consistent and user-friendly responses.
As AI continues advancing, innovative testing strategies like this will be used in the future in order to maintain the required standards, quality and reliability of AI applications.
