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

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

5 sosie program 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 5

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

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

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

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

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

11 Software brittleness 11

12 12

13 Software plasticity hypothesis 13

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

15 Specification: data and properties fun : Function assert abs(fun(.5) ) < 0.05 assert abs(fun(.4) ) < 0.05 assert abs(fun(.3) ) < 0.05 l l The test input data specifies the input domain 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 17

18 Automatic Synthesis of Sosies l 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 Four strategies l l l l random wittgenstein: replace with variables that have the same name reaction: replace with variables that have the same type steroid: reaction + rename variables

19 Experimental data #test cases compile 1me test 1me #assert coverage #statement Junit % EasyMock % Dagger (core) % JBehave- core % Metrics % commons- collec1ons % commons- lang % commons- math % clojure NA NA 71%

20 nb of trial: nb of compile: nb of sosie: (10%) don t compile don t pass all test cases sosies 20

21 Computation diversity Goal: unpredictability of execution flow Computation monitoring: method calls diversity variable diversity A.foo() IndexedCollection.retainAll(Collection) AbstractCollectionDecorator.retainAll(Collection) AbstractCollectionDecorator.decorated() other calls original call IndexedCollection.reindex() sosie call other calls... 21

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

23 Conclusion Sosies exist for all programs Sosies can exhibit computation diversity Next steps variability-aware execution is computational diversity unbounded?

24 References Zeyuan Allen Zhu, Sasa Misailovic, Jonathan A. Kelner, Martin C. Rinard: Randomized accuracyaware program transformations for efficient approximate computations. POPL 2012: Eric Schulte, Jonathan Dorn, Stephen Harding, Stephanie Forrest, Westley Weimer: Post-compiler software optimization for reducing energy. ASPLOS 2014: Frederick B Cohen: Operating system protection through program evolution. Computers & Security 12, 6 (1993):

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26 Sosies on line MDMS simple blog app JS on client and server sides Server side stack JS Java DB environment MDMS RingoJS Rhino JVM OS Redis DB 26

27 Sosies on line Monoculture multiple instances for performance load balancer Internet http request Nginx load balancer config 0 config 0 config 0 all instances are clones config 0 config 0 config 0 27

28 Sosies on line Diversified deployment All server instances are different Combine natural and artificial diversity Internet http request Nginx load balancer config 1 config 2 config 3 config 4 config 5 config 6 28

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30 Reactions graph R1 (int) code (boolean) R2 (boolean) code (int) Reactions graph one node per reaction there is an edge between n1 and n2 if n2.in_context == n1.in_context n1.out_context 30

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32 Two reactions graph (apache.common) Statement reactions graph #edges = #nodes = 863 graph-diameter = 3 avg path length = avg degree = Expression reactions graph #edges = #nodes = 1953 graph-diameter = 4 avg path length = avg degree =

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