Topics of Interest

Topics of Interest (include but are not limited to):

  • Testing AI applications
  • Methodologies for testing, verification and validation of AI applications
    • Process models for testing AI applications and quality assurance activities and procedures
    • Quality models of AI applications and quality attributes of AI applications, such as correctness, reliability, safety, security, accuracy, precision, comprehensibility, explainability, etc.
    • Whole lifecycle of AI applications, including analysis, design, development, deployment, operation and evolution
    • Quality evaluation and validation of the datasets that are used for building the AI applications
  • Techniques for testing AI applications
    • Test case design, test data generation, test prioritization, test reduction, etc.
    • Metrics and measurements of the adequacy of testing AI applications
    • Test oracle for checking the correctness of AI application on test cases
  • Tools and environment for automated and semi-automated software testing AI applications for various testing activities and management of testing resources
  • Specific concerns of software testing with various specific types of AI technologies and AI applications
  • Applications of AI techniques to software testing
  • Machine learning applications to software testing, such as test case generation, test effectiveness prediction and optimization, test adequacy improvement, test cost reduction, etc.
  • Constraint Programming for test case generation and test suite reduction
  • Constraint Scheduling and Optimization for test case prioritization and test execution scheduling
  • Crowdsourcing and swarm intelligence in software testing
  • Genetic algorithms, search-based techniques and heuristics to optimization of testing
  • Data quality evaluation for AI applications
  • Automatic data validation tools
  • Quality assurance for unstructured training data
  • Large-scale unstructured data quality certification
  • Techniques for testing deep neural network learning, reinforcement learning and graph learning