In this webcast, Dr. Mark Sherman summarizes the results of experiments that were conducted to see if various large language models (LLMs) could correctly identify problems with source code.
In this webcast, Brett Tucker, Dan Justice, and Matthew Butkovic will discuss the challenges to be expected with the realization of quantum computing capabilities.
Robinson, K., and Turri, V., 2024: Auditing Bias in Large Language Models. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...
Sible, J., and Svoboda, D., 2022: Rust Software Security: A Current State Assessment. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Wilson, S., Place, P., and Korzec, K., 2023: The Seven Virtues of Reconciling Agile and Earned Value Management (EVM). Carnegie Mellon University, Software ...
DeCapria, D., 2024: Introduction to MLOps: Bridging Machine Learning and Operations. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Ozkaya, I., and Schmidt, D., 2024: Generative AI and Software Engineering Education. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Sherman, M., 2024: Using ChatGPT to Analyze Your Code? Not So Fast. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 1 ...
Smith, J., 2024: Incorporating Agile Principles into Independent Verification and Validation. Carnegie Mellon University, Software Engineering Institute's Insights ...
Wassermann, G., and Svoboda, D., 2023: Rust Vulnerability Analysis and Maturity Challenges. Carnegie Mellon University, Software Engineering Institute's Insights ...
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 ...
Novak, W., 2023: Acquisition Archetypes Seen in the Wild, DevSecOps Edition: Clinging to the Old Ways. Carnegie Mellon University, Software Engineering Institute's ...