program behavior characterization and clustering: an empirical study for failure clustering danqing...

Program Behavior Characterization and Clustering: An Empirical Study

for Failure Clustering

Danqing ZhangSchool of Software Engineering, Tongji University

November 4th, 2013

2

Outline

1. Motivation

2. Runtime Behavior Representation

3. Behavior Clustering

4. Results and Analysis

5. Summary

3

Outline

1. Motivation




5. Summary

4

Failure clustering

• The major work of failure clustering is to categorize different failed executions according to those induced by the same faults.

Motivation Runtime Behavior Representation Behavior Clustering Results and Analysis Summary

5

Programs and their executions

L1:

L494:L495: f1 (a, b) {L496: int temp;L497: temp = a;L498: a = b;L499: b = temp;L500: }L501: printf (“a = %d\n”, a);L502:

L561:}


L1:

L228:L229: func (x, y) {L230: int t;L231: t = x;L232: x = y;L233: y = t;L234: }L235: printf (“y = %d\n”, y);L236:

L321:

Source code of P1 Source code of P2

.……

.……

.……

.……

6


L1:

L494:L495: f1 (a, b) {L496: int temp;L497: temp = a;L498: a = b;L499: b = temp;L500: }L501: printf (“a = %d\n”, a);L502:

L561:}


L1:

L228:L229: func (x, y) {L230: int t;L231: t = x;L232: x = y;L233: y = t;L234: }L235: printf (“y = %d\n”, y);L236:

L321:

Source code of P1 Source code of P2

.……

.……

.……

.……

7

Failure clustering

• The major work of failure clustering is to categorize different failed executions according to those induced by the same faults.


• Programs having similar attributes (e.g. structural features, execution profiles) are assumed to have similar fault behaviors and failure behaviors.

8


• The similarities of runtime behaviors of programs approximate similarities of the effects of “fault-error-failure” chain on programs.

• If programs can be clustered based on their runtime behaviors, their failure behaviors can be clustered in the same way.


9

Our work

• Runtime behavior modeling• Behavior clustering• Experimental evaluation


10

Outline

1. Motivation




5. Summary

11

Assumptions

• Programs are structured.• Runtime environment of programs is fault free.• Runtime characterization is defined for the IA32 platform

and the target programs are assumed to be of 32 bit code.


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mov %esp, %ebpcall 8048344 <f1>push %ebpmov %esp, %ebplea 0xb (%eax), %edxpop %ebpret

BIP: Branch-instruction-based partition

Code sequence(at assembly-level)


CALLS: CALL-state

RETS: RET-state

A procedure calling exists during program execution.

BIP-based state Runtime characteristic

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mov %esp, %ebpjmp 804842C <main+0x94>jne 904841a <main+0x82>int $0x80

BIP: Branch-instruction-based partition

Code sequence(at assembly-level)


UJS: Unconditional-jump-state

The body of a loop is executed.

Runtime characteristicBIP-based state

CJFS (CJTS): Conditional-jump-with-false (true)-state

INTS: INT-state A software interrupt is generated during program execution.

If/else statements are executed.

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UJSCJFSCJFSCALLSCALLSRETSUJSCJFSCJFSCALLSCALLSRETSUJS

How to characterize the runtime behavior ?


State sequence Short sequence of states (K=5)

Occurrence

<UJS CJFS CJFS CALLS CALLS> 2

<CJFS CJFS CALLS CALLS RETS> 2

<CALLS CALLS RETS UJS CJFS> 1

<CALLS RETS UJS CJFS CJFS> 1

<RETS UJS CJFS CJFS CALL> 1

0

• K is denoted as the length of the short sequence of states.• The total number of whole K-mer combinations is . (When

K=5, .)• The runtime behavior is represented as: {0, … , , … , , … , , … , , …, , … , 0}

7776

K

K

K

K

K

K

K

K

K

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Runtime behavior


• Runtime behavior : Runtime spectrum• When the optimal K value is chosen, the runtime behavior can

be well represented.

16

Outline

1. Motivation




5. Summary

17

Similarity between two runtime behaviors


Two runtime behaviors

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SD ( siT , s jT )=∑𝑙=1

6K

b i ,𝑙 logbi ,𝑙

(bi , 𝑙+b j , 𝑙)/2+∑

𝑙=1

6K

b j , 𝑙 logb j , 𝑙

(b i ,𝑙+b j , 𝑙) /2

Similarity degree between two behaviors

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SD ( siT , s jT )=∑𝑙=1

6K

b i ,𝑙 logbi ,𝑙

(bi , 𝑙+b j , 𝑙)/2+∑

𝑙=1

6K

b j , 𝑙 logb j , 𝑙

(b i ,𝑙+b j , 𝑙) /2

• is symmetric and a non-negative value.

• A smaller value of indicates a higher similarity.

• if and only if two behaviors are exactly the same.

Similarity degree between two behaviors

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Behavior clustering


• A fuzzy clustering algorithm is used to cluster program behaviors.

FCS: fuzzy compactness and separation• A method of determining the optimal cluster number (OCN)

Hazard rate First order backward difference

21

Outline

1. Motivation




5. Summary

22

Implementation


① Obtain the runtime behaviors during program executions (SPEC CPU2000 and SPEC CPU2006)

② Cluster the runtime behaviors

③ Obtain the failure behaviors (by fault injection)

④ Evaluate the equivalence of a cluster in runtime characterization (clustering) to that in failure clustering

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Implementation : to obtain runtime behaviors


N inputs

.….…

…

1

M

PIN

Runtime behavior

State sequence

Runtime behavior

State sequence

Runtime behavior

State sequence1

N

.…

M programs N program executions

N runtime behaviors

Com

pile

& li

nk

…1

NN=99

M=22

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Implementation : to cluster runtime behaviors


Sets of runtime behaviors

MDS : Multidimensional scaling

Sim

ilarit

y de

gree

Obtain similarity degrees of each two behaviors

Runtime behavior clustering

.…

Runtime behaviorRuntime behavior

Runtime behavior1

N

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Implementation : to obtain failure behaviors


Fault type Description

MFC Missing function call

MIA Missing if construct around statements

MIFS Missing if construct plus statements

MLAC Missing “AND EXPR” in expression using as branch condition

MVAE Missing variable assignment using an expression

MVAV Missing variable assignment using a value

WPFV Wrong variable used in parameter of function call

MVIV Missing variable initialization using a value

ODC : Orthogonal Defect Classification

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Implementation : to obtain failure behaviors


…

1

N

Fault injection

.…Similarity degrees between each two failure behaviors

SD ( s𝑓 iT , s 𝑓 jT )=∑𝑙=1

4❑

𝑓 𝑏i ,𝑙 log𝑓𝑏i , 𝑙

( 𝑓𝑏i , 𝑙+ 𝑓 b j , 𝑙)/2+∑

𝑙=1

4❑

𝑓𝑏 j ,𝑙 log𝑓𝑏 j ,𝑙

( 𝑓𝑏 i ,𝑙+ 𝑓𝑏 j , 𝑙)/2

N program executions

Occurrence frequencies of four failure modes

NCorrect | Aborted | Hanged | Wrong

% % % %

𝑠𝑓 𝑖𝑇={𝑏1 ,𝑏2 ,𝑏3 ,𝑏4 }

Correct | Aborted | Hanged | Wrong% % % %

𝑠𝑓 𝑖𝑇={𝑏1 ,𝑏2 ,𝑏3 ,𝑏4 }

1Correct | Aborted | Hanged | Wrong

% % % %

𝑠𝑓 𝑖𝑇={𝑏 𝑓 1,𝑏 𝑓 2 ,𝑏 𝑓 3 ,𝑏 𝑓 4 }¿650,000

N=99

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Implementation : K and CN (cluster number)


• Runtime behaviors can be well represented when the optimal K value is chosen.

• Runtime behaviors can be well clustered when the optimal CN is chosen.

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Evaluation : runtime behavior is well represented?


• The case study: K=5 and CN=7• For simplicity: UJS = 0, CJFS = 1 CJTS = 2, CALLS = 3 RETS = 4, INTS = 5

?

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Evaluation : failure behaviors are well clustered?


MFC-fault injection

SPEC CPU2000 and SPEC CPU2006

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SD ( s𝑓 iT , s 𝑓 jT )=¿• The case study: K=5 and optimal cluster number (OCN)=7• is used to describe the similarity degree• The statistics of similarity degree of MFC-induced failure

behaviors in each cluster are:

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Total

Similarity degree

99.94% 98.53% 98.23% 99.79% 76.73% 99.59% 91.91% 94.96%

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K=2 K=3 K=4 K=5 K=6 K=7 K=8 K=9

OCN 3,6,9 3,7,9 3,8 3,5,7 3,5,9 3,7,9 3,6 3,6

The OCN (optimal cluster number) for all the Ks

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K=2 K=3 K=4

OCN 3 6 9 3 7 9 3 8

Total simila

rity degree (%)

MFC 94.76 96.22 96.25 94.91 96.33 96.03 94.01 93.27

MIA 95.71 97.43 97.37 95.92 97.41 96.83 94.46 93.84

MIFS 95.14 96.97 96.86 95.17 96.6 96.17 94.16 93.57

MLAC 95.17 97.21 96.32 95.21 96.64 96.49 94.62 93.39

MVAE 95.23 97.19 96.61 96.19 97.23 96.77 95.12 94.72

MVAV 95.47 97.61 96.9 95.17 97.56 96.65 94.86 93.37

MPFV 95.01 96.98 96.67 95.41 97.12 96.83 94.55 93.39

MVIV 96.19 97.16 96.91 96.05 97.69 96.12 94.14 94.12

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K=2 K=3 K=4

OCN 3 6 9 3 7 9 3 8

Total simila

rity degree (%)

MFC 94.76 96.22 96.25 94.91 96.33 96.03 94.01 93.27

MIA 95.71 97.43 97.37 95.92 97.41 96.83 94.46 93.84

MIFS 95.14 96.97 96.86 95.17 96.6 96.17 94.16 93.57

MLAC 95.17 97.21 96.32 95.21 96.64 96.49 94.62 93.39

MVAE 95.23 97.19 96.61 96.19 97.23 96.77 95.12 94.72

MVAV 95.47 97.61 96.9 95.17 97.56 96.65 94.86 93.37

MPFV 95.01 96.98 96.67 95.41 97.12 96.83 94.55 93.39

MVIV 96.19 97.16 96.91 96.05 97.69 96.12 94.14 94.12

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K=5 K=6 K=7

OCN 3 5 7 3 5 9 3 7 9

Total similarity degree (%)

MFC 95.43 95.54 94.96 95.26 95.63 95.44 96.42 97.16 95.23

MIA 95.66 96.39 96.17 96.18 96.28 96.14 96.43 98.25 95.6

MIFS 95.64 95.72 95.31 96.29 96.33 95.46 97.18 97.22 96.01

MLAC 95.41 96.22 96.24 96.42 97.3 97.05 95.88 98.11 97.33

MVAE 96.79 97.03 95.5 95.51 96.43 96.46 96.83 98.24 95.67

MVAV 96.09 96.21 95.43 96.28 96.66 95.52 97.17 98.03 96.65

MPFV 95.7 95.82 95.68 95.56 96.45 95.62 97.4 97.79 95.51

MVIV 95.68 95.78 95.44 96.62 97 95.54 97.81 97.93 95.27

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K=5 K=6 K=7

OCN 3 5 7 3 5 9 3 7 9


MFC 95.43 95.54 94.96 95.26 95.63 95.44 96.42 97.16 95.23

MIA 95.66 96.39 96.17 96.18 96.28 96.14 96.43 98.25 95.6

MIFS 95.64 95.72 95.31 96.29 96.33 95.46 97.18 97.22 96.01

MLAC 95.41 96.22 96.24 96.42 97.3 97.05 95.88 98.11 97.33

MVAE 96.79 97.03 95.5 95.51 96.43 96.46 96.83 98.24 95.67

MVAV 96.09 96.21 95.43 96.28 96.66 95.52 97.17 98.03 96.65

MPFV 95.7 95.82 95.68 95.56 96.45 95.62 97.4 97.79 95.51

MVIV 95.68 95.78 95.44 96.62 97 95.54 97.81 97.93 95.27

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K=8 K=9

OCN 3 5 3 5


MFC 96.4 95.38 96.34 94.61

MIA 97.64 95.42 97.07 95.36

MIFS 97.2 95.55 96.52 95.44

MLAC 96.6 96.22 96.83 95.47

MVAE 96.77 95.99 97.61 95.58

MVAV 96.67 96.63 96.85 94.83

MPFV 96.91 96.25 96.36 95.14

MVIV 96.74 96.41 97.61 94.71

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K=8 K=9

OCN 3 5 3 5


MFC 96.4 95.38 96.34 94.61

MIA 97.64 95.42 97.07 95.36

MIFS 97.2 95.55 96.52 95.44

MLAC 96.6 96.22 96.83 95.47

MVAE 96.77 95.99 97.61 95.58

MVAV 96.67 96.63 96.85 94.83

MPFV 96.91 96.25 96.36 95.14

MVIV 96.74 96.41 97.61 94.71

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• The total similarity degree of failure behavior clustering reaches the maximum when K=7.

• When K=7, the quality of failure behavior clustering based on the runtime behavior clustering is the highest.

39

Outline

1. Motivation




5. Summary

40

Summary


• Once the optimal K and CN are chosen, runtime behaviors are well represented

based on BIP failure behaviors can be clustered according

to the runtime behavior clustering

• Expand the range of K and CN• Analyze the effectiveness of clustering fault behaviors

according to the runtime behavior clustering

program behavior characterization and clustering: an empirical study for failure clustering danqing...

Documents

runtime characterization

failure behaviors

analysis summary4 programs

analysis summaryl1

printf y

analysis summary8

analysis summaryprograms

analysis summarycalls