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Data-flow analysis propagates facts about values or variable uses through a control-flow graph. Each program operation transforms incoming facts, and a join combines information from possible predecessor paths. A compiler can use the result to justify compiler optimization or issue warnings. Clang's introduction develops this method using sets of possible values and iteration to a fixed point.^[ref]

For example, live-variable analysis asks which current values might be read later before being overwritten. For a basic block $b$, with uses before any local definition in ${\mathrm{USE}}_b$ and definitions in ${\mathrm{DEF}}_b$, the backward equations areA use requires the incoming value; an intervening definition removes that requirement. Iterating from empty sets terminates for a finite graph and finite variable set because these equations only add facts. The result guides register allocation.

Static program analysis derives information about a computer program from its representation and programming language semantics, without executing each concrete input case. An analysis commonly approximates many possible executions at once. Data-flow analysis propagates such information between locations in a control-flow graph.^[ref]

For example, after if c then x=1 else x=2, an analysis can infer that x belongs to $\{1,2\}$ at the join and hence is nonzero. More complicated paths may force a less precise answer. A conservative warning can therefore flag a possible failure that no actual input reaches. The guarantee depends on what the approximation includes: failing to model an external call or integer overflow can invalidate an apparent proof.