ankur jain, program slice, static slice, dynamic slice, system dependency graph, slicing, program dependency graph

1. Indian Institute of Technology, Kharagpur
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Presented By-
Ankur Jain (13CS60D02)

2. Programmer’s Nightmare…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
If I change this statement in program:
What other statements would be affected?
(Impact analysis)
What other statement needs to be tested?
(Regression test)
The values live at this statement:
Defined where?
Modified Where?
(Manual Checking)
How can I abstract code?

3. Studies show….
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Software testing contributes more then
50% of cost and time of SDLC
Maintainers spend nearly 50% of their
time trying to understand the program
Source: http://www.ece.cmu.edu/~koopman/des_s99/sw_testing/

4. Try...
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Program Slicing!

5. Outline
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
 Introduction
 Types of Slicing
 Program Models
 Flow based
 Dependency based
 Slicing Algorithms
 Challenges
 Directions of research

6. Proposed by…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
 Proposed by Mark Weiser in 1981
 Chief scientist at Xerox PARC
 As his PhD thesis
 Father of ubiquitous computing
Mark D. Weiser
(July 23, 1952 - April 27, 1999)

7. Basic Concepts…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
“For statement S and variable V, the slice of program P
includes only those statements of P needed to capture the
behavior of V at S.”
Slicing Criterion (SC):
<S, V>
S = Point of interest in the program (statement)
V = Subset of variables used in the program
A program slice is a subset of a program

8. Example…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
1 begin
2 read(x, y)
3 total := 0.0
4 sum := 0.0
5 if x <= 1
6 then sum := y
7 else begin
8 read(z)
9 total := x*y*z
10 end
11 write(total, sum)
12 end
SC = <11, sum>
2 read(x, y)
4 sum := 0.0
5 if x <= 1
6 then sum := y
Slicing Criterion

9. Why is Program Slicing Useful?
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
• Program slices are more manageable for
testing and debugging
• During testing, debugging, or understanding a program,
most of the code in the program is irrelevant to what you
are interested in.
• Program slicing provides a convenient way of filtering out
“irrelevant” code.

10. Slice Variants…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Types of slices:
 Static
 Dynamic
Direction of slice:
 Forward
 Backward
Executability of slice:
 Executable
 Non-executable
Amorphous, etc.

11. Froward Slices Vs Backward Slices
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Statements which might affect
the value of variables in V
Statements which might be
affected by the values of
variables in V
int i, sum, prd;
1. read(i);
2. prd = 1;
3. sum = 0;
4. while (i<10)
5. sum = sum + i;
6. prd = prd * i;
7. i = i + 1;
8. write(sum);
9. write(prd);For SC = <9, prd>
Slice: {1, 2, 4, 6, 7}
For SC = <2, prd>
Slice: {6, 9}
Backward Slices:
Froward Slices

12. Static Slice | Dynamic Slice
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Using dynamic information
(an execution trace)
Debugging
Set of all statements that actually
affect the value of a variable at a
program point
Using static information
(a source program)
Regression Testing
Set of all statements that may
affect the value of a variable at a
program point

13. Example…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
int i, sum, prd;
1. read(i);
2. prd = 1;
3. sum = 0;
4. while (i<10)
5. sum = sum + i;
6. prd = prd * i;
7. i = i + 1;
8. write(sum);
9. write(prd);
Static Slice
{1, 2, 4, 6, 7}
Dynamic Slice
For Input ‘i’ = 15
{2}
For Slicing Criteria [SC] = <9, prd>
Flow based model:
Control flow
 Data flow
Dependency based model:
 Data Dependency
 Control Dependency
Slicing Criterion

14. Dependency Based Model
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Program Code
Control Flow
Graph
(CFG)
Data
Dependency
Graph (DDG)
Program
Dependency
Graph (PDG)
Forward
Dominance Tree
Control
Dependency
Graph (CDG)

15. Data Dependency Graph (DDG)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
int a, b, sum;
1. read(a);
2. read(b);
3. sum = 0;
4. while(a<8)
5. sum = sum + b;
6. a = a + 1;
7. write(sum);
8. sum = b;
9. write(sum);
4 5
6
7
8
9
21 3
Data Dependency Graph

16. Control Flow Graph (CFG)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
int a, b, sum;
1. read(a);
2. read(b);
3. sum = 0;
4. while(a<8)
5. sum = sum + b;
6. a = a + 1;
7. write(sum);
8. sum = b;
9. write(sum);
Start
1
Stop
4
5
3
6
2
8
7
9
True
False
True
True
True
True
True
True
True
True
True

17. Dominance
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Let X and Y be two nodes in a Control flow graph
X dominates Y
If and only if
Every path from Start to Y passes through X
Y forward dominates X
The forward Dominance Tree (FDT)
1. Vertices represent statement
2. Root node of tree is exit node of the CFG
3. Arcs represent immediate forward dominance

18. Forward Dominance Tree (FDT): Using CFG
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
9
4 6
7
5
8
2
3
1
Start
1
Stop
4
5
3
6
2
8
7
9
True
False
True
True
True
True
True
True
True
True
True

19. Control Dependency Graph: Using CFG & FDT
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
4
5 6
7
8
9
2
1
3
S
1. Y is control dependent on X
2. Path in the CFG from X to Y
3. Doesn’t contain the
immediate forward
dominator of X
Y
X
X
Ifdom(4)=7
X
Y

20. Control Dependency Graph (CDG)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
int a, b, sum;
1. read(a);
2. read(b);
3. sum = 0;
4. while(a<8)
5. sum = sum + b;
6. a = a + 1;
7. write(sum);
8. sum = b;
9. write(sum);
4
5 6
7
8
9
2
1
3
S

21. Program Dependency Graph (PDG)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
int a, b, sum;
1. read(a);
2. read(b);
3. sum = 0;
4. while(a<8)
5. sum = sum + b;
6. a = a + 1;
7. write(sum);
8. sum = b;
9. write(sum);
Union of CDG and DDG
1
4
6
5
8
29
3
7
Data dependence
Control dependence
Limitation: A PDG can model programs with a single
function Not suitable for inter-procedural slicing

22. System Dependency Graph Model: by- Horwitz
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Basic Idea:
Connect PDGs with auxiliary dependence edges
• Parse source code - one procedure at a time.
– Construct the CDG for each procedure including main.
• Add actual and formal parameter nodes:
– Connect using parameter-in, parameter-out edges
• Represent function calls
– Using call edges
• Find data dependencies:
– Perform data flow analysis of the CDGs
– Connect data dependence edges
• Add summary edges
Steps in SDG Construction

23. System Dependency Graph (SDG)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Control Dependence
Data Dependence
Call, Parameter−in,
Parameter−out
Summary Edge
bin = b
ain = a
entry add
Entry main
a = 0
b = 1
add(a, b)
a =ain
b = bin
a = a+b
c = aout
aout = a
main(){
int a, b;
a = 0;
b = 1;
c=add(a, b);
}
void add(int a, int b)
{
a = a + b;
return a;
}

24. Slicing an SDG: Two phase algorithm
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Proposed by Horwitz et al
• Pass1: From the slice point:
• Traverse backward along all edges except
parameter-out edges
• Mark the reached vertices
• Pass 2: From vertices marked in Pass 1
• Traverse backwards along all edges:
• Except call and parameter-in edges

25. Slicing an SDG: Pass-1
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Control Dependence
Data Dependence
Call, Parameter−in,
Parameter−out
Summary Edge
main(){
int a, b;
a = 0;
b = 1;
c=add(a, b);
}
void add(int a, int b)
{
a = a + b;
return a;
}
entry main
a = 0
b = 1
add(a, b)
entry add
a =ain
b = bin
a = a+b
aout = a
c = aout
bin = b
ain = a
Slice Point
Except parameter-out edges

26. Slicing an SDG: Pass-2
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Control Dependence
Data Dependence
Call, Parameter−in,
Parameter−out
Summary Edge
main(){
int a, b;
a = 0;
b = 1;
c=add(a, b);
}
void add(int a, int b)
{
a = a + b;
return a;
}
entry
main
a = 0
b = 1 add(a, b)
entry add
a =ain
b = bin
a = a+b
aout = a
c = aout
bin = b
ain = a
Slice Point
Except call and parameter-in edges

27. Dynamic Slicing: Example cont…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Consider the following program:
Computing:
product of odd (p), sum of even (s)
Execution Trace, N=3
1,2,3,4 //intial
5,6,8,9 //i=1
5,6,7,9 //i=2
5,10 //i=3, end
Which part of the
program is responsible
for computing sum?

28. Dynamic Slicing: Example cont…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
We need to compute a backward slice with slicing criterion
<(10, s)>
Dependencies:
Dynamic Data Dependence:
which variable assignment was propagated to the value of `s' ?
Dynamic Control Dependence:
which are the conditional branches that executed till line 10
and in what order?

29. Dynamic Slicing: Example cont…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
For N=3

30. The Dynamic Slice w.r.t. (10,s)
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp

31. Slicing Tools
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
• Unravel : Static slicing tool for ANSI C
• WET : Whole Execution Traces, dynamic slicing
• CodeSurfer : Commercial static slicing tool for C
• Performs data flow and control dependence analysis
• Indus : Static slicer for Java
• Available for Eclipse via Kaveri plugin
• JSlice : Dynamic slicing tool for Java
• SPYDER: Debugging tool
• Performs both static and dynamic slice

32. Further Reduction in Size of Slice…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
Amorphous Slice:
 No Syntax Preservation
 Retaining the semantic property
Applications:
 Re-engineering
 Component Re-use
 Program Comprehension
 Testing & Maintenance
 Program Integration

33. Amorphous Slice: Example
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
for(i = 0,sum = a[0], biggest = sum; i<19; sum = a[++i])
if (a[i+1] > biggest)
{
biggest = a[i+1];
average = sum/20;
}
Amorphous slice
for(i = 1, biggest = a[0]; i<20; ++i)
{
if (a[i]>biggest)
biggest = a[i];
}
Traditional slice
for(i = 0,sum = a[0], biggest = sum; i<19;
sum = a[++i])
if (a[i+1] > biggest)
{
biggest = a[i+1];
}
Slicing Criterion
Loop unrolling

34. Challenges: For further reading…
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
 Slicing Object-Oriented Programs
 Effect of Exceptions on Control Flow
 Slicing UML Models
 Slicing of threaded programs
 Slicing Concurrent and Distributed Programs

35. Directions of Research
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp
A Novel Fault Localization Technique

36. References
[1] M Weiser. 1984. “Program slicing”
IEEE Transactions on Software Engineering 10(4):352–57
[2] F. Tip. 1995. A survey of program slicing techniques. Journal of
Programming Languages 3(3): 121–89
[3] D. P.Mohapatra, R.Mall, and R. Kumar. 2006.
“ An overview of slicing techniques for object-oriented
programs” Informatica 30(2):253–77
[4] G. B.Mund, R.Mall, and S. Sarkar. 2003. “Computation of intra-procedural
dynamic program slices. Information and Software Technology 45(8):499–512.
Indian Institute of Technology, Kharagpur
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp

37. Questions
Indian Institute of Technology, Kharagpur
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp

38. Indian Institute of Technology, Kharagpur
Date : 27 March-2014 Ankur Jain, Dept of Computer Sc & Engg., IIT Kgp