WALA includes several types of solvers applicable for various styles of dataflow analysis. You may find one of these helpful for setting up and solving a particular problem.

The simplest solver is the fixed point solver, DefaultFixedPointSolver. This solver simply manages a worklist of "statements", and "evaluates" statements until there are no further changes to the solution.

The main implementation benefits of using the WALA solver are a space-efficient representation of dependencies between statements, and the ability to evaluate statements based on various approximations to topological order. The latter feature is crucial for rapid convergence in some problems.

WALA's default IR construction and flow-insensitive pointer analysis, along with several other analyses, use the common fixed-point solver at the lowest level.

The com.ibm.wala.dataflow package provides a layer on top of the fixed-point solver to facilitate implementation of Killdall-style dataflow frameworks. The abstractions here help set up a dataflow system induced over nodes and/or edges of a graph.

To solve backwards dataflow problems, simply reverse graph edges using GraphInverter.invert(), and set up flow functions appropriately.

The TabulationSolver provides an implementation of the RHS algorithm for Sharir-Pneuli functional context-sensitive dataflow analysis, including IFDS. The TabulationSolver implementation has been tuned for space efficiency, and includes various extensions to help with features such as exceptional control flow.

To solve backwards dataflow problems, start with the BackwardsSupergraph.