Predicting Data Dependences for Slice Inspection ...€¦ · Predicting Data Dependences for Slice...
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Predicting Data Dependences for Slice Inspection Prioritization Yiji Zhang and Raul Santelices University of Notre Dame
Transcript of Predicting Data Dependences for Slice Inspection ...€¦ · Predicting Data Dependences for Slice...
Predicting Data Dependences for Slice Inspection Prioritization
Yiji Zhang and Raul Santelices University of Notre Dame
Outline • Introduction • Technique • Evaluation • Future work and Conclusion
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Introduction • Definitions
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
data dependence: statement y depends on x iff 1) x writes to variable v (def) 2) y reads from v (use)
Introduction • Definitions
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
example: data dependence (4,7,p) or simply (4,7)
Introduction • Definitions
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
program slicing: affecting statements slicing criterion: value and point of interest
Introduction • Definitions
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
slicing criterion: value p at line 7
Introduction • Definitions
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
slicing criterion: value p at line 7 slice: lines 1, 2, 3, 4, 5, 6 and 7
Introduction • Motivation
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
Q: Which statement to examine first?
Introduction • Related Work
– M. Sridharan, S. J. Fink, and R. Bodik, “Thin slicing,” in Proc. of PLDI, Jun. 2007, pp. 112–122
– X. Zhang, N. Gupta, and R. Gupta, “Pruning dynamic slices with confidence,” in PLDI, 2006, pp. 169–180
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Outline • Introduction • Technique • Evaluation • Future work and Conclusion
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Technique • PRIODU Process
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Program
D-U Analyzer data dependences (d1,u1)
(d2,u2) (d3,u3) (d4,u4) (d5,u5)
……
data dependencesd
Technique • PRIODU Process
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Program
D-U Analyzer
Probabilities, Ranking
data dependences
PRIODU dependence (d,u):
compute P(d,u)
(d1,u1), P(d1,u1) (d2,u2), P(d2,u2) (d3,u3), P(d3,u3) (d4,u4), P(d4,u4) (d5,u5), P(d5,u5)
……
P(d,u) : Coverage Probability (estimated)
):p ( , )compute P((d,u, ))
)
Technique • PRIODU Process
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Program
D-U Analyzer
Probabilities, Ranking
data dependences
PRIODU dependence (d,u):
compute P(d,u)
p ( , )compute P((d,u, ))
P(d,u) : Coverage Probability (estimated)
):)
decreasing P(d,u)
(d3,u3), P(d3,u3) (d2,u2), P(d2,u2) (d1,u1), P(d1,u1) (d5,u5), P(d5,u5) (d4,u4), P(d4,u4)
……
Technique Coverage Probability P(d,u) = Preach × Palias 1) Preach: Reaching probability 2) Palias: Alias probability
EN d u
d u
Memory
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EN: entry of the program
Technique 1) Preach = PEN d Pd u
for PEN d: src=EN, tgt=d for Pd u: src=d, tgt=u
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); } example: data dep (4,7,p)
;
;P(succeeds(6,4)) = 1.0
P(reaches(6,7)) = 0.5
Simplifying assumption: All successors equally likely
Goal: P(d,u) = Preach × Palias achrea h_______
Technique 2) Palias: only for d u
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d u
Memory
P2Set (points-to set): all possible memory locations
Palias: degree of overlap
Goal: P(d,u) = Preach × Palias aliaaas______
Technique 2) Palias: only for d u
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d u
Memory
P2Set(d)
P2Set (points-to set): all possible memory locations
Palias: degree of overlap
Goal: P(d,u) = Preach × Palias aliaaas______
Technique 2) Palias: only for d u
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d u
Memory
P2Set(u)
P2Set (points-to set): all possible memory locations
Palias: degree of overlap
Goal: P(d,u) = Preach × Palias aliaaas______
Technique 2) Palias: only for d u
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d u
Memory
P2Set (points-to set): all possible memory locations
Palias: degree of overlap
Palias =
Goal: P(d,u) = Preach × Palias aliaaas______
1 4 1
Technique 2) Palias: only for d u
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1. main(int a, int b){ 2. int *p; 3. if (…) 4. p = &a; else 5. p = &b; 6. if (…) 7. swap (p, &a); else 8. if (…) 9. a = b; else 10. swap (&b, &a); 11. print (a,b); }
12. swap (int *p1, int *p2){ 13. int temp = *p2; 14. *p2 = *p1; 15. *p1 = temp; }
example: dependence (1,14,b)
P2Set(d) = {b} (d) =
P2Set(u) = {a, b} p;P=
Goal: P(d,u) = Preach × Palias aliaaas______
mp == *p1; = tempp;
Outline • Introduction • Technique • Evaluation • Future work and Conclusion
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Evaluation • Goal: assess prediction effectiveness of PRIODU
– Compare with random order of the dependences
• Research questions: RQ1: Overall accuracy RQ2: Accuracy per inspection budget
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Evaluation • Experiment Setup
• Two Java subjects from SIR • Tool: DUA-Forensics – PRIODU extension
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Subject Description LOC Data Dep. Tests
Schedule1 Task scheduler 290 381 2650
NanoXML XML parser 3497 11508 214
Table 1 Subjects, Data Dependences, and Tests
Evaluation • Methodology
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(d1,u1), P(d1,u1) (d2,u2), P(d2,u2) (d3,u3), P(d3,u3) (d4,u4), P(d4,u4)
…… PRIODU
our predicted ranking
Program, Dependences
ranking
Evaluation • Methodology
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PRIODU
Program, Dependences
Test Suite Runner
Test Suite
observed ranking
our predicted ranking
Fi = occurrence frequency in [0,1]
(d1,u1), P(d1,u1) (d2,u2), P(d2,u2) (d3,u3), P(d3,u3) (d4,u4), P(d4,u4)
……
(d2,u2), F2 (d1,u1), F1 (d4,u4), F4 (d3,u3), F3
……
ranking
observed rankingo
Evaluation • Methodology
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PRIODU
Program, Dependences
Test Suite Runner
Test Suite
Error Estimator
observed ranking
our predicted ranking
Prediction Error
(d1,u1), P(d1,u1) (d2,u2), P(d2,u2) (d3,u3), P(d3,u3) (d4,u4), P(d4,u4)
……
(d2,u2), F2 (d1,u1), F1 (d4,u4), F4 (d3,u3), F3
……
Evaluation • Error Estimator
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# PRIODU Predicted
Runtime Observed
1 (d1,u1) (d2,u2)
2 (d2,u2) (d1,u1)
3 (d3,u3) (d4,u4)
4 (d4,u4) (d3,u3)
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(How we evaluate PRIODU)
Evaluation • Methodology
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PRIODU
Program, Dependences
Test Suite Runner
Test Suite
Error Estimator
observed ranking
our predicted ranking
Prediction Error
Evaluation • compare with the expected error of random
order
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dep
Random Ranking
Observed Ranking
D ith:
Evaluation • Expected error of random order
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dep
Random Ranking
Observed Ranking
D ith:
i-1 Case 1: Errordep(i) = (i-1)/2D
Evaluation
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dep
Random Ranking
Observed Ranking
ith: D
• Expected error of random order
Case 1: Errordep(i) = (i-1)/2D
Case 2: Errordep(i) = 0
Evaluation
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dep
Random Ranking
Observed Ranking
Case 1: Errordep(i) = (i-1)/2D ith:
D Case 2: Errordep(i) = 0
Case 3: Errordep(i) = (D-i)/2D
• Expected error of random order
D-i
Evaluation •
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Case 1 Case 2 Case 3
Evaluation • RQ1: Overall accuracy (based on average
error)
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Subject PRIODU Error Random Error Schedule1 20.9% 33.3% NanoXML 5.9% 33.3%
Evaluation • RQ2. Accuracy per fraction of the ranking
34 Ranking errors from the top of each ranking for Schedule1
Error: 39.51%
Error: 18.90%
Evaluation • RQ2. Accuracy per fraction of the ranking
35 Ranking errors from the top of each ranking for Schedule1
Evaluation • RQ2. Accuracy per fraction of the ranking
36 Ranking errors from the top of each ranking for NanoXML
Outline • Introduction • Technique • Evaluation • Future Work and Conclusion
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Future Work and Conclusion Future Work
– Case studies and user studies for debugging – Improve the precision of the model
• Reaching probabilities using branch prediction • Experiment with dynamic analysis
– Other applications • Impact analysis • Information flow analysis
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Future Work and Conclusion Conclusion PRIODU: (1) prioritize data dependences (2) especially good at the top of the ranking
Q & A
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