The October 23 virtual workshop will feature presentations on dataset generation, exploration, preparation, and testing for ensuring data quality when training AI systems.

The Software Engineering Institute is a leader in researching complex solutions, connecting AI, cyber, and software strategies for maximum impact.

Walsh, M., Schulker, D., Ross, D., and Cortese, D., 2024: Cyber-Physical Sensing to Extend the National Intelligence, Surveillance, and Reconnaissance Mesh. Carnegie ...

DeCapria, D., 2024: Introduction to MLOps: Bridging Machine Learning and Operations. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...

Klieber, W., and Flynn, L., 2024: Evaluating Static Analysis Alerts with LLMs. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...

Ozkaya, I., and Schmidt, D., 2024: Generative AI and Software Engineering Education. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...

Robinson, K., and Turri, V., 2024: Auditing Bias in Large Language Models. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...

Sherman, M., 2024: Using ChatGPT to Analyze Your Code? Not So Fast. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 15 ...

Wassermann, G., and Svoboda, D., 2023: Rust Vulnerability Analysis and Maturity Challenges. Carnegie Mellon University, Software Engineering Institute's Insights ...

Morrow, T., and Nicolai, M., 2022: The Zero Trust Journey: 4 Phases of Implementation. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...

This report describes the TSP and how it was developed. Starting with a brief background discussion of software quality, the report provides an overview of the basic elements of teamwork. The Team ...

Firesmith, D., 2019: System Resilience: What Exactly is it?. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 16, 2025 ...