Diversify presentation by Benoit Baudry, Simon Allier, Martin Monperrus

1. Tailored Source Code
Transformations to Synthesize
Computationally Diverse Program
Variants
Benoit Baudry, Simon Allier, Martin Monperrus

2. • This talk is about the generation of very large
quantities of sosie programs
2

3. sosie program
• Given a specification S
3

4. sosie program
4
• Given a specification S
• Given a program P
that conforms to S
specified
correct
behavior
bugs, vulnerabilities
expected behavior

5. sosie program
5
• Given a specification S
• Given a program P
that conforms to S
• A sosie of P is a
variant of P that also
conforms to S
a sosie

6. Motivation
• Explore brittelness vs.
plasticity of software
• Large quantities of
diverse variants
• Moving target
• Failure detection
computation failure diversity 6
diversity

7. Software brittleness
7
SRSLSLRSRLLSSRRLRL
G. Berry. « A la chasse aux bugs, la maladie du certain » (8 juin 2011)

8. Software brittleness hypothesis
8
SRSLSLRSRLLSSRRLRL
G. Berry. « A la chasse aux bugs, la maladie du certain » (8 juin 2011)

9. Software brittleness hypothesis
9
SRSLSLRSRLLSSRRLRL
SRSLSLSSRLLSSRRLRL
G. Berry. « A la chasse aux bugs, la maladie du certain » (8 juin 2011)

10. Software brittleness hypothesis
10
SRSLSLRSRLLSSRRLRL
SRSLSLSSRLLSSRRLRL
G. Berry. « A la chasse aux bugs, la maladie du certain » (8 juin 2011)

11. Software brittleness
11

12. 12

13. Software plasticity hypothesis
13

14. Software plasticity hypothesis
14
SRSLSLRSRLLSSRRLRL
SRSLSLSSRLLSSRRLRL
Rinard et al.
ICSE’10,
FSE’11
POPL’12,
PLDI’14
sosie

15. Specification: data and properties
fun : Function
assert abs(fun(.5) - 0.25) < 0.05
assert abs(fun(.4) - 0.16) < 0.05
assert abs(fun(.3) - 0.09) < 0.05
l The test input data specifies the input domain
l The assertions specify the level of abstraction

16. Research questions
Do sosies exist?
Can we automatically synthesize them?
What are effective transformations?
16

17. Sosiefication process
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20. Automatic Synthesis of Sosies
l We add/deleted/replace a given statement by another
one and see whether all assertions remain satisfied
l we pick code from the same program
l Four strategies
l random
l wittgenstein: replace with variables that have the same name
l reaction: replace with variables that have the same type
l steroid: reaction + rename variables

21. Experimental data
19
#test
cases
#assert
coverage
#statement
compile
1me
test
1me
Junit
721
1535
82%
2914
4.5
14.4
EasyMock
617
924
91%
2042
4
7.8
Dagger
(core)
128
210
85%
674
5.1
11.2
JBehave-­‐core
485
1451
89%
4984
5.5
22.9
Metrics
214
312
79%
1471
4.7
7.7
commons-­‐
collec1ons
1121
5397
84%
9893
7.9
22.9
commons-­‐lang
2359
13681
94%
11715
6.3
24.6
commons-­‐math
3544
9559
92%
47065
9.2
144.2
clojure
NA
NA
71%
18533
105.1
185

22. 20
nb of trial: 298938
nb of compile: 81394
nb of sosie: 28805 (10%)
don’t compile
don’t pass all test cases
sosies

23. Computation diversity
• Goal: unpredictability of
execution flow
• Computation monitoring:
• method calls diversity
• variable diversity
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A.foo()
IndexedCollection.retainAll(Collection)
AbstractCollectionDecorator.retainAll(Collection)
AbstractCollectionDecorator.decorated()
other calls
original call
IndexedCollection.reindex()
...
sosie call
other calls

24. 22
Easymock: 465 sosies
Dagger: 481 sosies
Junit: 446 sosies

25. Conclusion
• Sosies exist
• for all programs
• Sosies can exhibit computation diversity
• Next steps
• variability-aware execution
• is computational diversity unbounded?
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https://github.com/DIVERSIFY-project/sosies-generator
http://diversify-project.eu/sosiefied-programs/

26. References
• Zeyuan Allen Zhu, Sasa Misailovic, Jonathan A.
Kelner, Martin C. Rinard: Randomized accuracy-aware
program transformations for efficient
approximate computations. POPL 2012: 441-454
• Eric Schulte, Jonathan Dorn, Stephen Harding,
Stephanie Forrest, Westley Weimer: Post-compiler
software optimization for reducing energy.
ASPLOS 2014: 639-652
• Frederick B Cohen: Operating system protection
through program evolution. Computers Security
12, 6 (1993): 565–584.
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27. 25

28. Sosies on line
• MDMS
• simple blog app
• JS on client and server sides
• Server side stack
• JS
• Java
• DB
• environment
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MDMS
RingoJS
Rhino
JVM
Redis DB
OS

29. Sosies on line
• Monoculture
• multiple instances
for performance
• load balancer
• all instances are
clones
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Internet
http request
Nginx load balancer
config 0 config 0 config 0
config 0 config 0 config 0

30. Sosies on line
• Diversified
deployment
• All server instances
are different
• Combine natural
and artificial
diversity
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Internet
http request
Nginx load balancer
config 1 config 2 config 3
config 4 config 5 config 6

31. 29

32. Reactions graph
• Reactions graph
• one node per reaction
• there is an edge between n1 and n2 if
n2.in_context == n1.in_context ∨
n1.out_context
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R1
(int)
code
(boolean)
R2
(boolean)
code
(int)

33. 31

34. Two reactions graph (apache.common)
• Statement reactions graph
• #edges = 12304
• #nodes = 863
• graph-diameter = 3
• avg path length = 1.466
• avg degree = 14.257
• Expression reactions graph
• #edges = 37650
• #nodes = 1953
• graph-diameter = 4
• avg path length = 1.162
• avg degree = 19.278
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