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FIGURE 16. QUESTION 7 MODEL CODES AND THEMES
ChatGPT Codes
Claude Codes
1. Emphasis on COTS and modularity 2. Integration of systems engineering principles 3. Agile and incremental development approaches 4. Strategic alignment with organizational goals 5. Importance of technical expertise and training 6. Focus on risk management and mitigation 7. Encouragement of innovation and creativity 8. Use of prototyping to verify technology 9. Collaboration and integration across disciplines 10. Tailored approaches based on specific needs
1. Leveraging COTS products for cost-effectiveness 2. Modularity enables flexibility and integration 3. Prototyping reduces risk and facilitates transition 4. Agile methodologies enable rapid development 5. Strong technical foundation within government staff 6. Tailoring approach based on program needs 7. Balancing COTS and custom development 8. End-to-end systems engineering approach 9. Iterative development for consistent progress 10. Leveraging existing technologies for rapid deployment
Question 7 Themes
1. COTS and Modularity: • Leveraging cost-effective, flexible components for integration 2. Agile and Iterative Development: • Rapid, incremental progress for consistent results 3. Technical Expertise and Tailoring: • Adapting approach based on program needs and staff skills 4. Systems Engineering and Prototyping: • End-to-end approach, reducing risk through prototyping 5. Balancing Innovation and Existing Technologies: • Encouraging creativity while leveraging proven solutions
Decreasing the use of NRE was not explicitly stated in the code lists or the description provided by the theme synthesis. This can be attributed to the wording of the question or the absence of NRE as an explicitly stated example. Upon further inspection during the tenet alignment analysis, the ChatGPT model noted, “A common theme was the minimization of NRE through the reuse of existing designs and technologies.” This approach reduces cost and development time and enables quicker technology updates, matching the tenet’s objectives. Additionally, a manual search of the entire dataset indicated five mentions of the desire to reduce nonrecurring engineering—organizations that stressed this were the NRO, SpRCO, and USSOCOM. Finally, the data provided in the theme analysis were compared to the NVivo theme analysis. Figure 17 includes the common themes from
NVivo, which align closely with those found by the LLMs. The tenet is covered thoroughly based on the analysis, but a recommendation would be to include prototyping and end-to-end systems engineering principles. These recommendations were captured in later sections of this study.
FIGURE 17. Q7 NVIVO THEMES
Q7: NVivo Themes 1. COTS 2. Systems Engineering 3. End users 4. Mature Technology
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Defense ARJ, Summer 2025, Vol. 32 No. 2: 132—193
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