In artificial intelligence testing, there is an increased focus on enhancing the efficiency of test prioritization methods within deep learning systems. Subsequently, the DeepAbstraction algorithm has recently become one of the leading techniques in this area. It employs a box-abstraction concept, the efficiency of which depends on the tau parameter, the clustering parameter, that influences the size of these boxes. The conclusion of the previous experiments using tau values of 0.4 or 0.05 has failed to produce optimal results among all experiments. This highlights a significant challenge in the DeepAbstraction framework concerning the appropriate selection of the tau parameter. The selection of the tau value is extremely crucial, given its decisive effect on box size and, subsequently, the stability and efficacy of the framework. Addressing this challenge, we propose a methodology called combined parameterized boxes. This approach leverages the collective verdicts of monitors with various tau values to evaluate network predictions. We assign appropriate weights to these verdicts to ensure that no single verdict influences the decision-making process, thereby ensuring balance. Furthermore, we propose multiple strategies for integrating the weighted verdicts of monitors into a conclusive verdict, such as mean, max, product, and mode. The results of our investigation demonstrate that our approach can notably boost the DeepAbstraction framework's performance. Compared to the leading algorithms, DeepAbstraction++ consistently outperforms its competitors, delivering an increase in performance between 2.38% and 7.71%. Additionally, Deep-Abstraction++ brings remarkable stability to the process, addressing a significant shortcoming of the earlier version of DeepAbstraction.