Software engineering

Safety Assurance for AI-enabled Systems

Rigorous, evidence-based methods for assuring the safety of AI-enabled systems. Automated safety evidence generation; data-driven specification refinement; formal verification; requirement repair; self-adaptive safety assurance.

Cyber-Physical Systems

Methods for modeling and analyzing autonomous systems that interact with the physical world. Self-driving cars and robots; uncertainty-aware scenario generation; domain-specific abstractions; simulation-based testing.

Neuro-Symbolic Software Engineering

Combining AI and formal reasoning for trustworthy, explainable software-engineering workflows. Formal validation of AI outputs; LLM-based requirements analysis; AI-assisted verification; consistent data generation.

Automated software analysis and testing

Leveraging static and dynamic program analysis techniques to automate the quality assurance for software; automated bug detection; automated test generation; software security and vulnerability detection.

Internet-scale software

Resource management in cloud computing; inter-cloud frameworks and management; software architectures for Internet-of-things; security and trust in Internet-of-things.

Mining software repositories

Mining the historical data that is produced during the development of software systems to yield actionable insights; data mining; statistical regression analysis; machine learning.

Model-driven engineering

Modeling languages; concerns, aspects, viewpoints; advanced separation of concerns; model queries and transformations; code generators; model-based analysis; model-based reuse; model management; concern-driven development.

Requirements engineering

Elicitation, specification, analysis and validation of requirements; model-driven requirements engineering; goal-oriented modeling; requirements reuse; User Requirements Notation (URN).