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