Software Engineering Institute

The U.S. Department of Defense (DoD) and federal agencies are increasingly acquiring software-intensive systems instead of building them with internal resources. However, acquisition programs frequently have difficulty identifying the critical software acquisition activities, deliverables, risks, and opportunities.

The SEI is taking the initiative to develop an AI engineering discipline that will lay the groundwork for establishing the practices, processes, and knowledge to build new generations of AI solutions.

The SEI created an AISIRT to ensure that the Department of Defense and other federal agencies develop, adopt, and use AI effectively and securely to safeguard the security of the nation.

To support DoD mission success, the SEI’s Trust Lab advances the streamlined development of trustworthy and human-centered AI engineering practices by focusing on mission goals and prioritizing warfighter needs.

The SEI is advancing the professional discipline of AI engineering through the latest academic advancements at Carnegie Mellon University.

SEI researchers have applied causal learning to help the Department of Defense identify factors that increase software costs and to provide guidance to control them.

This study identifies the technologies and areas of research that are most critical for enabling future software systems. The technology roadmap that resulted from this work is intended to guide the research efforts of the software engineering community toward future systems that are safe, predictable, and evolvable.

Software for mission- and safety-critical systems, such as avionics systems in aircraft, is growing larger and more expensive. The Architecture Analysis and Design Language (AADL) addresses common problems in the development of these systems, such as mismatched assumptions about the physical system, computer hardware, software, and their interactions that can result in system problems detected too late in the development lifecycle.

AI Engineering focuses on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. The body of knowledge will be a standardization of this emergent discipline and will guide practitioners in implementing AI systems.

We offer an approach that reduces risk and simplifies the selection and acquisition of big data technologies when you acquire and develop big data systems.