In compiler theory, a reaching definition is a definition of a variable that may reach a particular point in a program without another definition of the same variable occurring along the intervening control-flow path.^[1] For example, in the following code:

d1 : y := 3
d2 : x := y

d1 is a reaching definition for d2. In the following, example, however:

d1 : y := 3
d2 : y := 4
d3 : x := y

d1 is no longer a reaching definition for d3, because d2 kills its reach: the value defined in d1 is no longer available and cannot reach d3.

The similarly named reaching definitions analysis is a data-flow analysis that statically determines which definitions may reach a given point in the code. Because of its simplicity, it is often used as a canonical example of data-flow analysis in compiler textbooks.^[2] The data-flow confluence operator used is set union, and the analysis is a forward data-flow analysis. Reaching definitions can be used to compute use-def chains.^[3]

The data-flow equations used for a given basic block in reaching definitions are:

In other words, the set of reaching definitions going into consists of all reaching definitions coming from 's predecessors, . consists of the basic blocks with control flow edges leading into . The reaching definitions coming out of are the reaching definitions entering , minus those whose variables are redefined in , together with any new definitions generated within .^[4]

For a generic instruction, we define the and sets as follows:

• , a set containing the definition generated by the instruction
• , the set of other definitions of killed by the instruction

where is the set of all definitions that assign to the variable . Here is a unique label attached to the assigning instruction; thus, the domain of values in reaching definitions consists of these instruction labels.

Reaching definitions are usually calculated using an iterative worklist algorithm.^[5]

Input: control-flow graph CFG = (Nodes, Edges, Entry, Exit)

// Initialize
for all CFG nodes n in N,
    OUT[n] = emptyset; // can optimize by OUT[n] = GEN[n];

// put all nodes into the changed set
// N is all nodes in graph,
Changed = N;

// Iterate 
while (Changed != emptyset)
{
    choose a node n in Changed;
    // remove it from the changed set
    Changed = Changed -{ n };

    // init IN[n] to be empty
    IN[n] = emptyset;

    // calculate IN[n] from predecessors' OUT[p]
    for all nodes p in predecessors(n)
         IN[n] = IN[n] Union OUT[p];

    oldout = OUT[n]; // save old OUT[n]
    
    // update OUT[n] using transfer function f_n ()
    OUT[n] = GEN[n] Union (IN[n] -KILL[n]);

    // any change to OUT[n] compared to previous value?
    if (OUT[n] changed) // compare oldout vs. OUT[n]
    {    
        // if yes, put all successors of n into the changed set
        for all nodes s in successors(n)
             Changed = Changed U { s };
    }
}

• Dead-code elimination
• Loop-invariant code motion
• Reachable uses
• Static single assignment form

• Aho, Alfred V.; Sethi, Ravi & Ullman, Jeffrey D. (1986). Compilers: Principles, Techniques, and Tools. Addison Wesley. ISBN 0-201-10088-6.
• Appel, Andrew W. (1999). Modern Compiler Implementation in ML. Cambridge University Press. ISBN 0-521-58274-1.
• Cooper, Keith D. & Torczon, Linda. (2005). Engineering a Compiler. Morgan Kaufmann. ISBN 1-55860-698-X.
• Muchnick, Steven S. (1997). Advanced Compiler Design and Implementation. Morgan Kaufmann. ISBN 1-55860-320-4.
• Nielson F., H.R. Nielson; , C. Hankin (2005). Principles of Program Analysis. Springer. ISBN 3-540-65410-0.