# Live-variable analysis

In compilers, live variable analysis (or simply liveness analysis) is a classic data-flow analysis to calculate the variables that are live at each point in the program. A variable is live at some point if it holds a value that may be needed in the future, or equivalently if its value may be read before the next time the variable is written to.

## Example

Consider the following program:
The set of live variables between lines 2 and 3 is {b, c} because both are used in the multiplication on line 3. But the set of live variables after line 1 is only {b}, since variable c is updated later, on line 2. The value of variable a is not used in this code.
Note that the assignment to a may be eliminated as a is not used later, but there is insufficient information to justify removing all of line 3 as f may have side effects (printing b * c, perhaps).

## Expression in terms of dataflow equations

Liveness analysis is a "backwards may" analysis. The analysis is done in a backwards order, and the dataflow confluence operator is set union. In other words, if applying liveness analysis to a function with a particular number of logical branches within it, the analysis is performed starting from the end of the function working towards the beginning (hence "backwards"), and a variable is considered live if any of the branches moving forward within the function might potentially (hence "may") need the variable's current value. This is in contrast to a "backwards must" analysis which would instead enforce this condition on all branches moving forward.
The dataflow equations used for a given basic block s and exiting block f in live variable analysis are the following:
    ${\mbox{GEN}}[s]$: The set of variables that are used in s before any assignment in the same basic block.
    ${\mbox{KILL}}[s]$: The set of variables that are assigned a value in s (in many books that discuss compiler design, KILL (s) is also defined as the set of variables assigned a value in s before any use, but this does not change the solution of the dataflow equation):
    ${\mbox{LIVE}}_{in}[s]={\mbox{GEN}}[s]\cup ({\mbox{LIVE}}_{out}[s]-{\mbox{KILL}}[s])$
    ${\mbox{LIVE}}_{out}[final]={\emptyset }$
    ${\mbox{LIVE}}_{out}[s]=\bigcup _{p\in succ[s]}{\mbox{LIVE}}_{in}[p]$
    ${\mbox{GEN}}[d:y\leftarrow f(x_{1},\cdots ,x_{n})]=\{x_{1},...,x_{n}\}$
    ${\mbox{KILL}}[d:y\leftarrow f(x_{1},\cdots ,x_{n})]=\{y\}$
The in-state of a block is the set of variables that are live at the start of the block. Its out-state is the set of variables that are live at the end of it. The out-state is the union of the in-states of the block's successors. The transfer function of a statement is applied by making the variables that are written dead, then making the variables that are read live.

## Second example

* // in:; predecessor blocks: none
* b1: a = 3;
* b = 5;
* d = 4;
* x = 100; //x is never being used later thus not in the out set {a,b,d}
* if a > b then
* // out: {a,b,d} //union of all (in) successors of b1 => b2: {a,b}, and b3:{b,d}
* // in: {a,b}; predecessor blocks: b1
* b2: c = a + b;
* d = 2;
* // out: {b,d}
* // in: {b,d}; predecessor blocks: b1 and b2
* b3: endif
* c = 4;
* return b * d + c;
* // out:
The in-state of b3 only contains b and d, since c has been written. The out-state of b1 is the union of the in-states of b2 and b3. The definition of c in b2 can be removed, since c is not live immediately after the statement.
Solving the data flow equations starts with initializing all in-states and out-states to the empty set. The work list is initialized by inserting the exit point (b3) in the work list (typical for backward flow). Its computed in-state differs from the previous one, so its predecessors b1 and b2 are inserted and the process continues. The progress is summarized in the table below.
| processing | out-state | old in-state | new in-state | work list |
| b3 | | | {b,d} | (b1,b2) |
| b1 | {b,d} | | | (b2) |
| b2 | {b,d} | | {a,b} | (b1) |
| b1 | {a,b,d} | | | |
Note that b1 was entered in the list before b2, which forced processing b1 twice (b1 was re-entered as predecessor of b2). Inserting b2 before b1 would have allowed earlier completion.
Initializing with the empty set is an optimistic initialization: all variables start out as dead. Note that the out-states cannot shrink from one iteration to the next, although the out-state can be smaller than the in-state. This can be seen from the fact that after the first iteration the out-state can only change by a change of the in-state. Since the in-state starts as the empty set, it can only grow in further iterations.