Software Quality Assurance for Artificial Intelligence

Description

This special issue focuses on quality assurance challenges and solutions specific to artificial intelligence systems. As AI becomes increasingly integrated into critical applications, ensuring software quality through rigorous testing, validation, and assurance practices is essential for building reliable and trustworthy AI systems.

Potential topics

  • Quality assurance methodologies for AI systems
  • Testing and validation of machine learning models
  • AI system reliability and robustness
  • Software engineering practices for AI development
  • AI safety and security testing
  • Performance evaluation of AI systems
  • Continuous integration and deployment for AI
  • Regulatory compliance and standards for AI software