Ranking & Metrics
Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page.
Research Impact Score:0.80
Contributing Best Scientists:16
Papers published by Best Scientists24
Research Ranking (Computer Science)907
Conference Call for Papers
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