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.
Software Architecture Patterns for Robustness
A Platform-Independent Model for DevSecOps
Using the Quantum Approximate Optimization Algorithm (QAOA) to Solve Binary-Variable Optimization Problems
Trust and AI Systems
A Dive into Deepfakes
Challenges and Metrics in Digital Engineering
The 4 Phases of the Zero Trust Journey
DevSecOps for AI Engineering
Undiscovered Vulnerabilities: Not Just for Critical Software
Explainable AI Explained
Model-Based Systems Engineering Meets DevSecOps
Incorporating Supply-Chain Risk and DevSecOps into a Cybersecurity Strategy
Software and Systems Collaboration in the Era of Smart Systems
Securing the Supply Chain for the Defense Industrial Base
Building on Ghidra: Tools for Automating Reverse Engineering and Malware Analysis
Envisioning the Future of Software Engineering
Implementing the DoD's Ethical AI Principles
Walking Fast Into the Future: Evolvable Technical Reference Frameworks for Mixed-Criticality Systems
Software Engineering for Machine Learning: Characterizing and Understanding Mismatch in ML Systems
A Discussion on Automation with Watts Humphrey Award Winner Rajendra Prasad
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