Download presentation

Presentation is loading. Please wait.

Published byEloise Durrance Modified about 1 year ago

1
Data-Flow Analysis Framework Domain – What kind of solution is the analysis looking for? Ex. Variables have not yet been defined – Algorithm assigns a set of assertions to each node/edge Approximation – Useful data-flow properties are never 100% accurate Rice’s Theorem, from 1953 – Lower approximation is called a MUST analysis Set of solutions found is smaller than the set of actual solutions – Upper approximation is called a MAY analysis Set of solutions found may be larger than the set of actual solutions

2
Data-Flow Analysis Framework Direction – Forwards: For each node/edge, computes information about past behavior – Backwards: For each node/edge, computes information about future behavior Transfer Functions – JOIN: Specifies how information from adjacent nodes /edges is propagated MAY: Union of adjacent edges MUST: Intersection of adjacent edges – GEN: Specifies which possible solutions are generated at the node/edge – KILL: Specifies which possible solutions are removed at that node/edge

3
Data-Flow Algorithm 1.Start at the top (bottom) of the CFG Forwards: top Backwards: bottom 2.At each node compute: (JOIN() – KILL(node)) U GEN(node) At each branch: Follow all paths, in any order, up to node where path merges Once all paths up to merge are complete, continue at merge node 3.If all JOIN edges are not yet computed, use empty set (MAY) universal set (MUST) 4.For loops: repeat until the solution for all nodes in loop doesn’t change Called the “fixed-point”

4
Liveness A variable is live at a node if its current value can be read during the remaining execution of the program Domain: program variables Backwards MAY analysis

5
Liveness Example

6
Liveness Transfer Functions Exit – GEN(exit) = { } – KILL(exit) = { } Conditions and Output – GEN(stmt) = Set of all variables appearing in the statement – KILL(stmt) = { } Assignment – GEN(assignment) = Set of all variables appearing on the right-hand side – KILL(assignment) = Set with variable being assigned to Declaration – GEN(declaration) = { } – KILL(declaration) = Set of variables being declared Other – GEN(other) = { } – KILL(other) = { }

7
Liveness Example { } { x } {x, z} {x} {x, y} {x} {x, z} { } true false true false START END

8
Liveness Application Memory Allocation – Since y and z are never live at the same time, they can share the same memory location Performance Optimization – Assignment, z = z – 1, is never used

9
Liveness Application Bug Checking (z = z – 1) is dead on assignment – FindBugs says : “This instruction assigns a value to a local variable, but the value is not read or used in any subsequent instruction. Often, this indicates an error, because the value computed is never used. “

10
Data-Flow Framework Summary Generic framework for different analyses Each analysis defines – Domain – Approximation – Direction – Transfer Functions Used for optimization, verification, and testing

11
Reaching Definitions An assignment statement that may have defined the value of a variable at a particular node Domain: assignment statements Forwards MAY analysis

12
Reaching Definitions Transfer Functions Assignments – GEN(assignment) = the statement itself – KILL(assignment) = Statements that assigned to the same variable Declaration – GEN(decl) = the statement itself – KILL(decl) = 0 Other – GEN(other) = 0 – KILL(other) = 0

13
Reaching Definitions Example

14
{a1, a2, a3, a5, a6} Reaching Definitions Example x = input output x x = x - y y > 3 x > 1 y = x/2 z = x - 4 x = x/2 z > 0 z = z - 1 { } a1 a2 a3 a4 a5 a6 {a1} {a1, a2} {a1, a2, a3, a4, a5} {a2, a4, a5} {a1, a2, a3, a5, a6} {a2, a3, a6} {a1, a2, a3, a5, a6} {a2, a3} {a1, a2, a3, a4} START END {…}

15
Reaching Definitions Applications FindBugs: “NP: Possible null pointer dereference” Debugging – “Slicing” tools Following chains of Reaching Definitions backwards to track down bugs Basis for Information Flow Security – Discuss in lectures on Security

16
Exercise Compute the reaching definitions for each node, using the iterative dataflow algorithm. Show solutions for each loop iteration. 1:function test(r1, r2, r3, r4, r5) { 2:while(r1 < 10) { 3: r1 = r1 + 1; 4: r5 = r1 * 2; 5: if((r1 % 2) == 0) 6: r2 = 0; 7: else 8: r2 = r2 + 1; 9: r4 = r2 + r1; 10:} 11:return r4 + r5; 12: }

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google