Traditional call-graph construction algorithms conflate the computation of possible runtime types with the actual resolution of (virtual) calls.

This tangled design impedes supporting complex language features and APIs and making systematic trade-offs between precision, soundness, and scalability.

It also impedes implementation of precise downstream analyses that rely on type information.

To address the problem, we propose Unimocg, a modular architecture for call-graph construction that decouples the computation of type information from resolving calls.

Due to its modular design, Unimocg can combine a wide range of different call-graph algorithms with algorithm-agnostic modules to support individual language features.

Moreover, these modules operate at the same precision as the chosen call-graph algorithm with no further effort. Additionally, Unimocg allows other analyses to easily reuse type information from the call-graph construction at full precision.

We demonstrate how Unimocg enables a framework of call-graph algorithms with different precision, soundness, and scalability trade-offs from reusable modules.

Unimocg currently supports ten call-graph algorithms from vastly different families, such as CHA, RTA, XTA, and $k$-$l$-CFA.

These algorithms show consistent soundness without sacrificing precision or performance.

We also show how an immutability analysis is improved using Unimocg.