Topics of Interest

Topics of interest include, but are not limited to:

1. Testing of AI

Methodologies, theories, techniques, and tools for testing, verification, and validation of AI
Test Oracle for testing AI
Tools and resources for automated testing of AI
Techniques for testing deep neural network learning, reinforcement learning, and graph learning

2. AI for Software Testing

AI techniques to software testing
AI applications to software testing
Human testers and AI-based testing
Crowdsourcing and swarm intelligence in software testing
Genetic algorithms, search-based techniques, and heuristics to optimize testing
Constraint programming for test case generation and test suite reduction
Constraint scheduling and optimization for test case prioritization and test execution scheduling

3. Large Language Models (LLMs)

Testing of Large Language Models (LLMs)
Quality evaluation and assurance for LLMs
LLMs for software engineering and testing
Fairness, ethics, bias, and trustworthiness for LLM applications

4. Data Quality and Policy

Data quality and validation for AI
Quality assurance for unstructured training data
Large-scale unstructured data quality certification
AI and data management policies

5. Domain-Specific Testing

Specific concerns of testing with domain-specific AI
Computer Vision Testing
Intelligent Chatbot Testing
Smart Machine (Robot/AV/UAV) Testing
Impact of GAI on education
Responsible AI testing