sei.cmu

Application of Large Language Models (LLMs) in Software Engineering: Overblown Hype or ...

Ozkaya, I., Carleton, A., Robert, J., and Schmidt, D., 2023: Application of Large Language Models (LLMs) in Software Engineering: Overblown Hype or Disruptive Change ...
sei.cmu

What is Explainable AI?

Turri, V., 2022: What is Explainable AI?. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 29, 2025, https://www.sei.cmu ...
sei.cmu

A Field Study of Technical Debt

Ernst, N., 2015: A Field Study of Technical Debt. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 29, 2025, https://www ...
sei.cmu

Probably Don’t Rely on EPSS Yet

Spring, J., 2022: Probably Don’t Rely on EPSS Yet. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 29, 2025, https ...
sei.cmu

Rust Vulnerability Analysis and Maturity Challenges

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

CERT Division

The CERT Division is a leader in cybersecurity. We partner with government, industry, law enforcement, and academia to improve the security and resilience of computer systems and networks. We study ...
sei.cmu

System Resilience: What Exactly is it?

Firesmith, D., 2019: System Resilience: What Exactly is it?. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 29, 2025 ...
sei.cmu

Artificial Intelligence (AI) Division

Leaders in defense and national security want to obtain the leap-ahead capabilities AI offers. At the same time, it is difficult to get AI right. As many as 85% of current AI deployments fail—failures ...
sei.cmu

A Hitchhiker’s Guide to ML Training Infrastructure

Palat, J., 2022: A Hitchhiker’s Guide to ML Training Infrastructure. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October ...
sei.cmu

Bridging the Gap between Requirements Engineering and Model Evaluation in Machine Learning

Turri, V., and Heim, E., 2022: Bridging the Gap between Requirements Engineering and Model Evaluation in Machine Learning. Carnegie Mellon University, Software ...
sei.cmu

Using ChatGPT to Analyze Your Code? Not So Fast

Sherman, M., 2024: Using ChatGPT to Analyze Your Code? Not So Fast. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 27 ...
sei.cmu

The Myth of Machine Learning Non-Reproducibility and Randomness for Acquisitions and ...

Mellinger, A., Justice, D., Connor, M., Gallagher, S., and Brooks, T., 2025: The Myth of Machine Learning Non-Reproducibility and Randomness for Acquisitions and ...