9th IEEE International Conference on Artificial Intelligence Testing (AITest 2027)

The 9th IEEE International Conference on Artificial Intelligence Testing (AITest 2027) explores advancements in artificial intelligence testing, including methodologies for verifying AI systems, AI-driven software testing, large language models, data quality, domain-specific AI testing, and responsible AI development.

Key Topics

• Quality assurance for unstructured training data
• Constraint programming for test case generation and test suite reduction
• Techniques for testing deep neural network learning, reinforcement learning, and graph learning
• Tools and resources for automated testing of AI
• LLMs for software engineering and testing
• AI and data management policies
• Human testers and AI-based testing
• Testing of Large Language Models (LLMs)
• Test Oracle for testing AI
• Computer Vision Testing
• Impact of GAI on education
• Genetic algorithms, search-based techniques, and heuristics to optimize testing
• Fairness, ethics, bias, and trustworthiness for LLM applications
• Constraint scheduling and optimization for test case prioritization and test execution scheduling
• Smart Machine (Robot/AV/UAV) Testing
• Quality evaluation and assurance for LLMs
• Intelligent Chatbot Testing
• Specific concerns of testing with domain-specific AI
• Methodologies, theories, techniques, and tools for testing, verification, and validation of AI
• Large-scale unstructured data quality certification
• Crowdsourcing and swarm intelligence in software testing
• AI applications to software testing

Who should Attend

• Data scientists
• Software engineers
• AI practitioners
• Quality assurance professionals
• Academic researchers
• Machine learning engineers
• Software testing professionals
• Industry leaders in AI and software development
• Policy makers in AI governance
• Cybersecurity experts