Model-Based Systems Engineering promises traceability from requirements through architecture to implementation, and that well-defined interfaces make integrating components across teams easier. In ...
Svoboda, D., 2026: The SEI CERT Coding Standard for Fortran. Software Engineering Institute blog, Accessed September 3, 2026, https://doi.org/10.58012/w9t7-6y96. This ...
PITTSBURGH, Pa., June 8, 2026—The Carnegie Mellon University Software Engineering Institute (SEI) and Accenture today released the Artificial Intelligence (AI) Adoption Maturity Model, an empirically ...
AI is rapidly changing how software is produced but not the need to engineer software to meet business and mission goals. AI is enabling developers to move from idea to implementation at incredible ...
This report analyzes nine security and zero trust principles from a study about the applicability of foundational security and zero trust principles to weapon systems. Zero trust is a security model ...
Hansen, J., Echeverría, S., Pons, L., Moreno, G., Lewis, G., and Zhan, L., 2025: Enhancing Machine Learning Assurance with Portend. Software Engineering Institute ...
Chick, T., Frye, B., and Reffett, A., 2025: The DevSecOps Capability Maturity Model. Software Engineering Institute blog, Accessed September 3, 2026, https://doi.org ...
Costa, D., and Trzeciak, R., 2023: The 13 Key Elements of an Insider Threat Program. Software Engineering Institute blog, Accessed August 31, 2026, https://doi.org/10 ...
The guide describes 22 best practices for mitigating insider threat based on the CERT Division's continued research and analysis of more than 3,000 insider threat cases. This seventh edition of the ...
Technical debt is a widely recognized software engineering concern that refers to the tradeoff between the short-term benefits of rapid delivery and the long-term value of developing a software system ...
Kazman, R., 2022: Tactics and Patterns for Software Robustness. Software Engineering Institute blog, Accessed September 2, 2026, https://www.sei.cmu.edu/blog/tactics ...
Consider a production line in which workers run heavy, potentially dangerous equipment to manufacture steel tubing. Company executives hire a team of machine learning (ML) practitioners to develop an ...
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