AI Test Modeling and Analysis for Intelligent Chatbot Mobile App - A Case Study on Wysa
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Proceedings - 6th IEEE International Conference on Artificial Intelligence Testing, AITest 2024
DOI
10.1109/AITest62860.2024.00024
First Page
132
Last Page
141
Abstract
The functionalities of AI-powered mobile apps or systems heavily depend on the given training dataset. The challenge, in this case, is that a learning system will change its behavior due to a slight change in the dataset. Current alternative approaches for evaluating these apps either focus on individual performance measurements such as accuracy, etc. Inspired by principles of the decision tree test method in software engineering, this paper provides a tutorial discussion on intelligent AI test modeling chat systems including basic concepts, validation process, testing scopes, approaches, and needs. The report is about an intelligent AI test modeling chatbot system that is built and implemented based on an innovative 3D AI test model for AI-powered functions in intelligent mobile apps to support model-based AI function testing, test data generation, auto test scripting and execution, and adequate test coverage analysis.
Keywords
3D Intelligent Chat Test Modeling, AI test Result Validation, Chatbots, Data Augmentation, Smart AI Chat System Testing, Test Generation
Department
Applied Data Science; Computer Engineering
Recommended Citation
Jerry Gao, Prerna Garsole, Radhika Agarwal, and Shengqiang Liu. "AI Test Modeling and Analysis for Intelligent Chatbot Mobile App - A Case Study on Wysa" Proceedings - 6th IEEE International Conference on Artificial Intelligence Testing, AITest 2024 (2024): 132-141. https://doi.org/10.1109/AITest62860.2024.00024
