AI Testing Call For Papers

IEEE AI Tests invites papers of original research on AI testing and reports of the best practices in the industry as well as the challenges in practice and research.
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, esign, development, deployment, operation and evolution
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
Statistical machine learning and data mining
Symbolic machine learning, decision trees and random forests, reinforcement learning
Evolutionary methods and Genetic algorithms
Multi-agent systems
Heuristic search algorithms
Cognitive computing
Knowledge management, expert systems
Automatic reasoning and theorem proving
Constraint Programming and Constraint Optimization
Metaprogramming, high-order functions, high-order logic
Programming by example, programming synthesis, etc
Specific concerns of software testing for various types of AI applications
Computer vision and object recognition in image, audio and video
Personalized recommendation systems, and business intelligence
Driverless vehicles and autonomous robotics
Intelligent diagnostic systems
Decision-making support systems
Prediction and forecast systems
Smart cities, smart home, healthcare, and medicine, etc
Natural language processing, and intelligent human machine interactions, etc
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
Multi-agent systems for testing and test services
Crowdsourcing and swarm intelligence in software testing
Genetic algorithms, search-based techniques and heuristics to optimization of testing
Knowledge-based and expert systems for software testing
Data quality checking for AI applications
Testing and quality assurance for unstructured training data
Automatic validation tool for training unstructured data and big data
Large-scale unstructured data quality certification

Follow the link if anyone has an interesting submission: IEEE AI Tests Conference 2021