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10 Benefits and 10 Challenges of Applying Large Language Models to DoD Software Acquisition

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posted on 2024-01-24, 23:26 authored by John RobertJohn Robert, Douglas SchmidtDouglas Schmidt

Department of Defense (DoD) software acquisition has long been a complex and document-heavy process. Historically, many software acquisition activities, such as generating Requests for Information \(RFIs), summarizing government regulations, identifying relevant commercial standards, and drafting project status updates, have required considerable human-intensive effort. However, the advent of generative artificial intelligence (AI) tools, including large language models (LLMs), offers a promising opportunity to accelerate and streamline certain aspects of the software acquisition process.  Software acquisition is one of many complex mission-critical domains that may benefit from applying generative AI to augment and/or accelerate human efforts. This blog post is the first in a series dedicated to exploring how generative AI, particularly LLMs like ChatGPT-4, can enhance software acquisition activities. Below, we present 10 benefits and 10 challenges of applying LLMs to the software acquisition process and suggest specific use cases where generative AI can provide value. Our focus is on providing timely information to software acquisition professionals, including defense software developers, program managers, systems engineers, cybersecurity analysts, and other key stakeholders, who operate within challenging constraints and prioritize security and accuracy.

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This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/AZS 5 Eglin Street Hanscom AFB, MA 01731-2100. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution.

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Copyright 2024 Carnegie Mellon University.

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