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