a modern C++ library for the manipulation of Boolean functions

6 Conclusions
6.1 Future developments
The efficiency of CORAL, and in particular of the portion of China that utilizes CORAL,
must still be improved. While the results of the experimental evaluation were decent,
we still haven’t been able to match the efficiency and the precision of the native China
implementation: we are currently able to match or beat the native implementation only
by applying upward approximation more intensively.
Instead of using a single Boolean data member to record whether each representation
〈n, E, D, L〉 is normalized or not, we may also consider recording all states of partial
normalization. For example, we may use multiple Boolean data members to record:
Whether we know that there are no entailed variables in n or not.
Whether we know that all variables in E do not occur in n or not.
Whether we know that there are no disentailed variables in n or not.
Whether we know that all variables in D do not occur in n or not.
... (etc.)
These informations may allow us to avoid unnecessary operations in various algorithms.
Both CORAL and China still need extensive debugging before release.