# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 3 Methodology
### Relevance to Environmental Assumptions  
This case study is particularly well-suited for our analysis for several reasons. First, the requirement to maintain safe separation distances is inherently tied to environmental assumptions such as wind conditions, geolocation accuracy, and communication latency.  
This dependency makes the case highly relevant for exploring how unvalidated or incorrect assumptions can influence requirement fulfillment and lead to the accumulation of technical debt. Second, this case study offers explicit mapping between environmental assumptions (A5-A16) and derived requirements (DR1-DR5). This makes it easier to analyze the impact of omitting or introducing incorrect assumptions, as well as to classify the resulting RTD. Third, it spans multiple technical domains, including aerodynamics, communication systems, and real-time computing, which underscores the interdisciplinary complexity of validating assumptions and maintaining robust requirements across diverse operational contexts. Finally, although the case centers on sUASs, the principles and challenges discussed, such as managing environmental assumptions, validating requirements, and addressing technical debt, apply to other cyber-physical systems (CPS) and safety-critical systems. This makes the case study a valuable reference for broader research and practice [34]. To support our analysis, Table 2 provides a summary of the key functional requirements (DR1-DR5) and their associated environmental assumptions (A5-A16) identified in the DroneResponse case study [34]. We omit the environmental assumptions (A1-A4) because they serve as system-wide operational assumptions rather than being directly tied to a specific derived requirement (DR1-DR5).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Requirement** & **Description** & **Environmental Assumptions** & **Type** \\ \hline \multirow{8}{*}{DR1: Geolocation Accuracy} & \multirow{8}{*}{Drones must determine the geolocation and neighboring drones’ positions with at least 99\% confidence within a 3D region.} & \multirow{8}{*}{-} & **A5:The extended Kalman filter (EKF) correctly estimates the drone’s latitude/longitude with a 68\% confidence interval.** & \\  & & & **A6: The EKF estimates altitude with similar accuracy.** & \\  & & & **A7: Geolocation errors follow a normal distribution and can be scaled to 99\% confidence.** & \\ \hline \multirow{8}{*}{DR2: Stopping Distance Without Considering its velocity.} & \multirow{8}{*}{Each drone must compute its maximum stopping distance under non-wind conditions, considering its velocity.} & \multirow{8}{*}{-} & **A8: All drones operate autonomously, eliminating human reaction delays.** & \\  & & & **A9: Communication latency over the Doodle Labs Smart Radio is between 6 and 60 ms.** & \\ \cline{1-1}  & & & **A10: Environmental factors affecting communication delays are negligible.** & \\ \hline \multirow{8}{*}{DR3: Impact of Wind on Stopping Distance} & \multirow{8}{*}{Drones must account for wind effects when computing stopping distance.} & \multirow{8}{*}{-} & **A11: Operators do not launch drones in unsafe wind conditions.** & \\ \cline{1-1}  & & & **A12: Sideways drift is minimal and remains within acceptable limits.** & \\ \cline{1-1}  & & & **A13: Tailwinds have a greater impact on stopping distance than other wind directions.** & \\ \cline{1-1}  & & & **A14: The Gazebo simulation model accurately represents drone behavior in wind conditions.** & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the requirements (DR1–DR5) and their associated environmental assumptions (A5–A16) identified in the DroneResponse project, adapted from [34].