# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### Geolocation Uncertainty (DR1)  
The first requirement states that drones must determine their geolocation and neighboring drones' positions with at least 99% confidence within a 3D region. This requirement is based on three environmental assumptions, i.e., EA5-EA7. One key assumption is that the extended Kalman filter (EKF) provides accurate geolocation estimates. EKF is a state estimation algorithm that fuses data from multiple sensors, such as GPS and IMU, to estimate position and velocity. The assumption implies that EKF provides sufficiently reliable and accurate estimates under expected operational conditions.  
\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 DR4: Projected Distance During Communication Delays & Drones must estimate the distance they and their neighbors will travel between status updates. & - & **A15** Every drone knows its physical dimensions, broadcasts this information upon startup, and notifies now drones upon their entry to the airspace. & Operational environment \\ \hline DR5: Maintaining Safe Minimum Separation & Drones must maintain a safe minimum separation distance at all times. & - & **A16** The tolerances in separation distance calculations are large enough to account for uncertainties and non-linear interactions. & Operational environment \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._If this assumption is omitted or incorrect (e.g., due to sensor noise or GPS errors), then the requirement itself becomes unrealistic because it is difficult to guarantee 99% confidence in geolocation accuracy. The RTD that could emerge in this scenario is that the requirement relies on faulty assumptions. Consequently, the system may miscalculate separation distances, which would increase collision risks. If identified earlier, this would require modifying the requirement (DR1) to either accept a lower confidence level or impose constraints on operating conditions, for example, that drones can only operate in clear weather or in areas with strong GPS coverage. However, real-world conditions may not consistently support these constraints, which makes assumption validation essential before including them in the requirement. If these assumptions are not properly tested earlier, issues may not surface until later stages of testing or post-deployment, when the assumptions are exercised under real conditions. This form of RTD necessitates late-stage requirements revisions and incurs consequences commonly described as \"interest\", such as additional testing cost and increased processing overhead. Developers may also need to modify algorithms, increase safety margins, and introduce backup localization methods.  
**RTD:** Failure to validate these assumptions before encoding them into the requirement (e.g., through designing and executing a series of field tests with specific physical drones could lead to an incorrect or unrealistic requirement (DR1). This type of debt is classified as _unvalidated assumption debt_, which refers to requirements that are based on unverified assumptions.