Software Engineering Institute (SEI) Podcast Series Members of Technical Staff at the Software Engineering Institute

Research and development of quantum computers continues to grow at a rapid pace. The U.S. government alone spent more than $800 million on quantum information science research in 2022. Thomas Scanlon, who leads the data science group in the SEI CERT Division, was recently invited to be a participant in the Workshop on Cybersecurity of Quantum Computing, co-sponsored by the National Science Foundation (NSF) and the White House Office of Science and Technology Policy, to examine the emerging field of cybersecurity for quantum computing. In this podcast from the Carnegie Mellon University Software Engineering Institute, Scanlon discusses how to create the discipline of cyber protection of quantum computing and outlines six areas of future research in quantum cybersecurity.

Far too often software programs continue to collect metrics for no other reason than that is how it has always been done. This leads to situations where, for any given environment, a metrics program is defined by a list of metrics that must be collected. A top-down, deterministic specification of graphs or other depictions of data required by the metrics program can distract participants from the potentially useful information that the metrics reveal and illuminate. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Will Hayes, who leads the Agile Transformation Team, and Patrick Place, a principal engineer on that team, discuss with principal researcher Suzanne Miller, how user stories can help put development in the context of who is using the system and lead to a conversation about why a specific metric is being collected. 

In working with software and systems teams developing technical products, Judy Hwang, a senior software engineer in the SEI CERT Division, observed that teams were not investing the time, resources and effort required to manage the product lifecycle of a successful product. These activities include thoroughly exploring the problem space by talking to users, assessing existing solutions, understanding the competition, and positioning the product to create value for customers. In this podcast from the Carnegie Mellon University Software Engineering Institute, Hwang talks with principal researcher Suzanne Miller about the importance of implementing foundational product management principles in software and systems development and offers resources for audience members who looking to strengthen their Agile product delivery practices.

The ability of artificial intelligence (AI) to partner with the software engineer, doctor, or warfighter depends on whether these end users trust the AI system to partner effectively with them and deliver the outcome promised. To build appropriate levels of trust, expectations must be managed for what AI can realistically deliver. In this podcast from the SEI’s AI Division, Carol Smith, a senior research scientist specializing in human-machine interaction, joins design researchers Katherine-Marie Robinson and Alex Steiner, to discuss how to measure the trustworthiness of an AI system as well as questions that organizations should ask before determining if it wants to employ a new AI technology.

In this podcast from the Carnegie Mellon University Software Engineering Institute, Bill Nichols and Julie Cohen talk with Suzanne Miller about how automation within DevSecOps product-development pipelines provides new opportunities for program managers (PMs) to confidently make decisions with the help of readily available data.
As in commercial companies, DoD PMs are accountable for the overall cost, schedule, and performance of a program. The PM’s job is even more complex in large programs with multiple software-development pipelines where cost, schedule, performance, and risk for the products of each pipeline must be considered when making decisions, as well as the interrelationships among products developed on different pipelines. Nichols and Cohen discuss how PMs can collect and transform unprocessed DevSecOps development data into useful program-management information that can guide decisions they must make during program execution. The ability to continuously monitor, analyze, and provide actionable data to the PM from tools in multiple interconnected pipelines of pipelines can help keep the overall program on track.

In the wake of the COVID pandemic, the workforce decentralized and shifted toward remote and hybrid environments. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Dan Costa, technical manager of enterprise threat and vulnerability management, and Randy Trzeciak, deputy director of Cyber Risk and Resilience, both with the SEI’s CERT Division, discuss how remote work in the post-pandemic world is changing expectations about employee behavior monitoring and insider risk detection.