## 7 Threats to Validity  
In this part, we address the factors that could potentially impact the outcome of this study. The main threats to validity of this study may arise due to the validity of the proposed method given in Section 4 and the validity of the case study given in Section 5.1. These factors are given under External Validity, Internal Validity, Construct Validity and Reliability categories along with the measures to mitigate these concerns.  
**External Validity:** In this study, the proposed method was tested through four scenarios in UAV context to support its generalizability and adaptability to different requirements. The best hardware options for each scenario were found to be STM32H7 series microcontrollers, NVIDIA Jetson Nano, Raspberry Pi 4, and NVIDIA Jetson Xavier NX, respectively. The best object detection model options were found as SSD Mobilenet v1 0.25 for STM32H7, YOLOFastestV2 for NVIDIA Jetson Nano and Raspberry Pi 4, and Complex-YOLOv3 for Jetson Xavier NX. We cross-validated the results of the case study by forming a rating table (Appendix E, Edge Hardware--Metrics Rating), where each criterion/metric of the hardware candidates are rated on a relative comparison, 1 to 5 scale. The proposed method's applicability should also be tested through a broader range of Edge AI applications such as smart home and healthcare systems that may have different requirements and operational goals to show its practical usage in different contexts and/or settings. Another issue is related to the validity of the proposed method. To mitigate this risk, we followed a validated meta-model structure and extended it to the quality of Edge AI applications.  
**Internal Validity:** Internal validity is related to the study's ability to establish a relationship between the proposed method and the observed outcomes. In the evaluation stage, we applied six fuzzy MCDA methods to find the best edge hardware options and AImodel options for each of the four scenarios. A threat to internal validity in this study can be related due to reliance on fuzzy MCDA techniques which may introduce the possibility of bias due to subjective weighting of criteria, which could affect the outcome of the results. To mitigate this risk, we conducted a focus session involving six experienced practitioners from the field to collaboratively participate in the criteria weighting process. Moreover, statistical analysis of expert scores was employed to identify and address discrepancies, to ensure a more balanced and objective weighting approach.  
**Construct Validity:** Construct validity concerns the degree to which the method accurately measures the concepts it aims to evaluate. To ensure this, we have adopted and extended an already validated quality meta-model [74], which enabled us to use and apply correct terms and relations in regard to quality specification and evaluation. In this study, construct validity may be threatened by the appropriateness of the criteria and metrics chosen to evaluate edge hardware and AI model candidates. There is a risk that the selected quality characteristics and metrics may not fully capture all relevant aspects regarding the UAV operational quality across proposed scenarios. To ensure this, we used the ISO/IEC 25010:2011 standard to guide the evaluation and used the characteristics and metrics that are commonly referenced in the literature for MAV and UAV context. In addition, we collected the senior practitioners' feedback on the relevant quality characteristics and metrics. We believe that we have covered the most significant quality characteristics and metrics.  
**Reliability:** Reliability is related to the consistency and repeatability of the results. In our study, due to the systematic approach that we have defined and utilized, by following the process flow given in Figure 3, the outcome of the case study is expected to produce the same outputs across four scenarios when tested with the same fuzzy MCDA methods under same goal, edge hardware candidates, AI model candidates, and criteria weights.