# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 5 Discussion and Implications  
The findings reveal that environmental assumptions play a crucial role in the emergence of RTD. The classification framework proposed in this study provides a new lens through which RTD can be identified and mitigated early in the software lifecycle. This work also complements the RTDQM model proposed by [PERSON] et al. [38]. While RTDQM focuses on quantifying RTD through measurable dimensions such as cost, effort, and system impact, this classification offers a causal perspective by tracing the origins of RTD to flawed, evolving, or missing environmental assumptions. Integrating our classification into RTDQM can support more nuanced assessments of RTD severity and help prioritize mitigation efforts based on their environmental root causes. This classification also complements existing RTD taxonomies, such as the Type 0-2 categorization by [PERSON] and [PERSON] [40], by focusing on the Underlying environmental assumptions involved in system modeling. While previous taxonomies emphasized smells, user needs, or mismatches, this work surfaces how the _source_of RTD may lie in unvalidated or evolving environmental assumptions. Although this classification was developed through a case study in the sUAS domain, we believe it applies to a wide range of cyber-physical and safety-critical systems, such as autonomous vehicles, industrial IoT, and medical devices. These domains similarly depend on assumptions about dynamic, uncertain, and evolving environments, which make them susceptible to the same forms of assumption-driven RTD. For example, in the medical domain, the control software of a personal insulin pump [41], which helps regulate blood glucose levels in diabetic patients, often relies on several implicit environmental assumptions about its operational environment. One requirement states that insulin should be delivered only when glucose levels are rising and the rate of increase is accelerating. This requirement depends on several key environmental assumptions, as follows: (1) the blood glucose sensor maintains high accuracy under varying conditions, (2) the insulin pump reliably actuates each time a dosage is triggered, and (3) sensor performance remains consistent over time without significant drift or degradation [41]. When such assumptions prove incorrect, are not adequately validated, or gradually break down over time, different forms of RTD can accumulate. Take Assumption 1, for example. If the accuracy of the glucose sensor is not properly verified, perhaps due to sensor wear, contamination, or calibration drift, the system may either falsely detect a rise in glucose or fail to catch a critical spike. In such cases, the requirement becomes effectively invalid, as it no longer reflects the behavior of the system in real-world conditions. Addressing this kind of issue may require the introduction of recalibration procedures or the adjustment of threshold parameters in the requirements. This situation represents _unvalidated assumption debt_, where the reliance on unverified environmental assumptions leads to costly and delayed requirement corrections. A similar issue results from Assumption 2; if Assumption 2 is incomplete or not explicitly specified, the requirement implicitly assumes flawless hardware performance. If mechanical faults disrupt actuation, insulin may not be delivered even when triggered, which could create a silent failure. This is an instance of _incomplete assumption debt_, where the omission of important environmental assumptions creates hidden vulnerabilities. In fact, failures of this nature have led to the recall of medical devices in the past [42]. Finally, Assumption 3 concerns the long-term stability of sensor performance. Over time, sensors may degrade due to environmental exposure, for example, yet the original requirement may continue to assume stable operation. As the mismatch between assumption and reality grows, system behavior becomes increasingly unreliable.