Software Engineering Institute (SEI) Podcast Series

AI components, whether traditional machine learning or generative AI, are embedded in many present-day systems. However, despite advances in automation, infrastructure, and tooling for developing AI components, many do not leave the prototype stage or reach production because they fail to meet overall system quality expectations. In our latest podcast from the Carnegie Mellon University Software Engineering Institute, Rachel Brower-Sinning and Robert Edman, both machine learning scientists in the SEI's Tactical Edge and AI-Enabled Systems Initiative, sit down with Grace Lewis, the initiative's lead and an SEI principal researcher, to discuss a quality model for machine learning components to support their proper testing and evaluation. The research team that developed the model also included Alex Derr, Sebastián Echeverría, and Ipek Ozkaya from the SEI, and, from Carnegie Mellon University, Kate Maffey. Colin Beaudoin, a PhD intern at the SEI in the summer of 2025 who is now an assistant professor at Fairfield University, also worked on the model.

The recent explosion in large language model (LLM) technology has highlighted the challenges of using public generative artificial intelligence tools in classified environments, especially for software analysis. Currently, software analysis falls on the shoulders of static analysis tools and manual code review, which tend to provide limited technical depth and are often time-consuming in practice. In our latest podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Ryan Karl, an SEI embedded engineer, and John Robert, deputy director of the SEI's Software Solutions Division, discuss their work on using LLMs in unclassified environments to rapidly develop tools that accelerate software analysis in classified environments with improved accuracy for certain software analysis tasks.

Software-defined warfare is today's reality for national security, shifting the emphasis in military operations from hardware to software. In the latest podcast from the Carnegie Mellon University Software Engineering Institute, SEI director Paul Nielsen recently sat down with Matthew Butkovic, technical director of Risk and Resilience in the SEI's CERT Division, to discuss the evolution of software-defined warfare and the ways in which software engineering practices can meaningfully address the challenges of implementing software on the battlefield.

In aviation, waypoints guide pilots through complex flight plans, providing some structure but maintaining flexibility. Kevin Dooley, a senior Agile transformation leader at the SEI, adopted this concept to solve one of defense acquisition's most persistent challenges: synchronizing dozens of interdependent teams without drowning in administrative noise and overhead. In the latest podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Dooley, who co-developed the Waypoints Framework with Air Force Major Adam Satterfield, sits down with Eileen Wrubel, SEI technical director for Smart Software Acquisition to discuss Waypoints and how it can help teams visualize their work and own processes so they can start collaborating.

Experimentation and validation of LLM performance is critical when building LLM-driven systems that must reliably deliver a service, from customer service chat bots to intelligence analysis tools. To help teams meet the need for rigorous evaluation methods, a research team in the SEI's AI Division led by Violet Turri has developed the Expanding Large Language Model Metrics (ELM) library, which is built on best practices for LLM evaluation and benchmarking. In the latest episode from the Carnegie Mellon University Software Engineering Institute Podcast Series, Turri sits down with Katie Robinson, a design researcher also in the SEI's AI division, to discuss the library, which turns evaluation from an ad-hoc process into a repeatable, extensible framework.