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Presented By: Krishna Balasubramanian

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1 Presented By: Krishna Balasubramanian
Precise Dynamic Slicing Algorithms Xiangyu Zhang, Rajiv Gupta and Youtao Zhang Presented By: Krishna Balasubramanian

2 Slicing Techniques? Static Slicing
Isolates all possible statements computing a particular variable Criteria: <v, n> Dynamic Slicing Isolates unique statements computing variable for given inputs Criteria: <i, v, n>

3 Example – Data dependences
Static Slice <10, z> = {1, 2, 3, 4, 7, 8, 9, 10} Dynamic Slice <input, variable, execution point> <N=1, z, 101> = {3, 4, 9, 10}

4 Slice Sizes: Static vs Dynamic
Program Statements Static Dynamic Static/Dynamic 126.gcc 585,491 51, ,614 7.72 099.go 95,459 16, ,382 3.14 134.perl 116,182 5, 6.85 31,829 2, 11.89 008.expresso 74,039 2, 6.72 Static slicing gives huge slices On an average, static slices much larger

5 Precise Dynamic Slicing
Data dependences exercised during program execution captured precisely and saved Only dependences occurring in a specific execution of program are considered Dynamic slices constructed upon users requests by traversing captured dynamic dependence information Limitation : Costly to compute

6 Imprecise Dynamic Slicing
Reduces cost of slicing Found to greatly increase slice sizes Reduces effectiveness Worthwhile to use precise algorithms

7 Precise vs Imprecise: Slice Size
Implemented two imprecise algorithms: Algorithm I and Algorithm II Imprecise increases the Slice Size Algorithm II better than Algorithm I

8 Precise Dynamic Slicing - Approach
Program executed Execution trace collected PDS involves: Preprocessing: Builds dependence graph by recovering dynamic dependences from program’s execution trace Slicing Computes slices for given slicing requests by traversing dynamic dependence graph

9 3 Algorithms Proposed Full preprocessing (FP) – Builds entire dependence graph before slicing No preprocessing (NP) No preprocessing performed Does demand driven analysis during slicing Caches the recovered dependencies Limited preprocessing (LP) Adds summary info to execution trace Uses demand driven analysis to recover dynamic dependences from compacted execution trace What do you think is better and why?

10 Comparison FP algorithm impractical for real programs
Runs out of memory during preprocessing phase Dynamic dependence graphs extremely large NP algorithm does not run out of memory but is slow LP algorithm is practical Never runs out of memory Fast

11 1) Full Preprocessing Edges corresponding to data dependences extracted from execution trace Added to statement level control flow graph Execution instances labeled on graph Uses instance labels during slicing Only relevant edges traversed

12 FP - Example Load to store edge on left labeled (1,1)
Load to store edge on right labeled (2,1) 1st/2nd instance of load’s execution gets value from 1st instance of execution of store on the left/right When load included in dynamic slice, not necessary to include both stores in dynamic slice. Instance Labels

13 FP - Example

14 FP - Example Dynamic data dependence edges shown
Edges labeled with execution instances of statements involved in data dependences Data dependence edges traversed during slice computation of Z used in the only execution of statement 16 is: (161, 143), (143, 132), (132, 122), (132, 153), (153, 31), (153, 152), (152, 31), (152, 151), (151, 31), (151, 41) Precise dynamic slice computed is: DS<x=6, z, 161> = {16,14,13,12,4,15,3} Compute the slice corresponding to the value of x used during the first execution of statement 15 ?? DS <x=6, x, 151> = Slice {4,15}

15 2) No Preprocessing Demand driven analysis to recover dynamic dependences Requires less storage compared to FP Takes more time Caching used to avoid repetitive computations Cost of maintaining cache vs repeated recovery of same dependences from trace

16 NP Example No dynamic data dependence edges present initially
To compute slice for z at only execution of st 16: single backward traversal of trace (161, 143), (143, 132), (132, 122), (132, 153), (153, 31), (153, 152), (152, 31), (152, 151), (151, 31), (151, 41) extracted

17 NP with Cache Data dependence edges added to program flow graph
Compute slice for use of x in 3rd instance of st 14 All dependences required already present in graph Trace not reexamined Compute slice for use of x by 2nd instance of st 10 Trace traversed again Additional dynamic data dependences extracted

18 3) Limited Preprocessing
LP strikes a balance b/w preprocessing & slicing costs Limited preprocessing of trace Augments trace with summary information Faster traversal of augmented trace Demand driven analysis to compute slice using augmented trace Addresses Space problems of FP Time problems of NP

19 LP – Approach Trace divided into trace blocks
Each trace block of fixed size Store summary of all downward exposed definitions of variable names & memory addresses Look for variable definition in summary of downward exposed definitions If definition found, traverse trace block to locate it Else, use size information to skip to start of trace block

20 Evaluation Execution traces on 3 different input sets for each benchmark computed Computed 25 different slices for each execution trace Slices computed wrt end of program’s execution End) Computed 25 slices at an additional point in program’s execution midpoint) for 1st input

21 Results – Slice sizes PDS Sizes for additional Input PDS sizes for 2nd & 3rd program inputs End are shown No. of statements in dynamic slice is small fraction of statements executed Different inputs give similar observations Thus, Dynamic slicing is effective across different inputs

22 Evaluation - Slice computation times
Compared FP, NPwoC, NPwC, and LP Cumulative execution time in seconds as slices are computed one by one is shown Graphs include both preprocessing times & slice computation times

23 Y-Axis: Cum Exec time(s)
Execution Times X-Axis: Slices Y-Axis: Cum Exec time(s)

24 Observations FP rarely runs to completion Mostly runs out of memory
NPwoC, NPwC and LP successful Makes computation of PDS feasible NPwoC shows linear increase in cumulative exec time with no. of slices LP cumulative exec time rises much more slowly than NPwoC and NPwC

25 Observations Cumulative times: NP vs LP Trace Blocks skipped by LP
Exec times of LP are 1.13 to 3.43 times < than NP Due to % of trace blocks skipped by LP Shows that limited preprocessing does pay off

26 LP (Precise) vs Algorithm II (Imprecise)
Slice Sizes: Slices computed by LP 1.2 to times smaller than imprecise data slices of Algorithm II Relative performance was similar Execution Times: @ End, total time taken by LP 0.55 to 2.02 times Algorithm II @ Midpoint, total time taken by LP is 0.51 to 1.86 times Algorithm II

27 Results Both have no memory problems Smaller slice sizes for LP
For large slices, execution time greater than imprecise For small slices, execution time less than imprecise

28 Summary Precise LP algorithm performs the best
Imprecise dynamic slicing algorithms are too imprecise, hence not an attractive option LP algorithm is practical Provides Precise Dynamic Slices at reasonable space and time costs

29 Thank you!

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