ISSTA 2016: Generating focused random tests using directed swarm testing

Transcript

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   Generating Focused Random Tests using Directed Swarm Testing Mohammad Amin

   Alipour, Alex Groce, Rahul Gopinath, Arpit Christi EECS, Oregon State University
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   Random Test Generator Test Generator Software Under Test (SUT) Oracle

   ( ,output) Test Features (e.g. name of APIs) OK NOT OK
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   Goal Spidermonkey Static Analysis tool Buggy Block JsFunfuzz Test suite

   - FOCUS : newly added/changed code, rarely covered code, statement suggested by static analysis
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   Hitting Fraction (HF) Hitting Fraction or Frequency of Coverage: Given

   a test suite TS and a coverage target t, the fraction of test cases in TS that cover t is called hitting fraction. • If TS has no test to cover t, HF(TS, t) = 0 • If all tests in TS cover t, HF(TS, t) = 1 Hitting Fraction of statements of YAFFS2 using yaffs2tester
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   Focused Random Tests Potential Applications: • Finding bugs in suspicious

   parts of code • Regression Testing Initial Test Suite Focused Test Suites Generating new test suites to increase hitting fraction (HF) of less-frequently covered targets.
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   Swarm Testing Random Test Generator Software Under Test (SUT) Oracle

   Test Features F1,F2,F3,F4.... (e.g. name of APIs) Save test case ( output) Pick a random subset of test features Alex Groce, Chaoqiang Zhang, Eric Eide, Yang Chen, John Regehr: Swarm testing. ISSTA 2012: 78-88 OK NOT OK
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   Triggers and suppressors • Target- Behavior of SUT, produced by

   test cases • Example- faults, coverage entities, mutant • Relationship between feature f and target t • Trigger • Suppressor • Irrelevant • Wilson Score of Confidence [ISSRE’13] Alex Groce, Chaoqiang Zhang, Mohammad Amin Alipour, Eric Eide, Yang Chen, John Regehr: Help, help, I'm being suppressed! The significance of suppressors in software testing. ISSRE 2013: 390-399 F1 F2 F3 F4 ….. T1 S T I I I T2 I I S I I T3 S S I T I …… I I S I T
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   Summary so far Program Swarm random generator F1 F2 F3

   …... S1 S2 S3 S4 S5 T1 T2 T3 T4 T5 F1 F2 F3 F4 ….. T1 S T I I I T2 I I S I I T3 S S I T I …… I I S I T
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   Directed Swarm Testing Program Swarm random generator F1 F2 F3

   …... Configuration Strategy S1 S2 S3 S4 S5 T1 T2 T3 T4 T5 F1 F2 F3 F4 ….. T1 S T I I I T2 I I S I I T3 S S I T I … … I I S I T F1,F2,…...
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    Methodology Swarm Testing (Seed run) Configuration Strategy Swarm Testing Coverage,Configuration

    Configuration
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    Half Swarm f1,f2 - triggers, f3,f4 - suppressors, f5 -

    f9 - irrelevants f1 f2 f6 f8 f5 f6 f7 f8 f9 Swarm generator f1 f2 f3 f4
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    No Supressors f1,f2 - triggers, f3,f4 - suppressors, f5 -

    f9 - irrelevants f1 f2 f5 f6 f7 f8 f9 f5 f6 f7 f8 f9 Swarm generator f1 f2 f3 f4
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    Triggers only f1,f2 - triggers, f3,f4 - suppressors, f5 -

    f9 - irrelevants f1 f2 f5 f6 f7 f8 f9 Swarm generator f1 f2 f3 f4
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    Multi-target Directed Swarm Testing Challenge: • A trigger for a

    target can be a suppressor for another one, or vice versa. Merging Strategies: • Round-robin (No merging) • Subsumption Merge a configuration to a stricter one • Aggressive Merging Tries to merge as many as non-conflicting configuration • Optimum Generates minimum set of configurations NP-Complete [RW] [RW] Edward L. Robertson and Catharine M. Wyss: Optimal Tuple Merge in NP-Complete, Technical Report TR599, Indiana University
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    Evaluation - SUT SUT LOC Fuzzer Description YAFFS2 15K Yaff2tester

    Flash file system GCC 4.4.7 860K Csmith C and C++ compiler Spidermonkey 118K Jsfunfuzz Javascript engine for Mozilla SUT # Features Seed time(min) Run time(min) YAFFS 43 15 5 GCC 25 60 10 Spidermonkey 171 30 10 Subjects Experimental parameters
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    Evaluation: Directed vs Undirected Swarm for single target Hitting Fraction:

    Directed vs Undirected
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    Evaluation of Directed Swarm Testing YAFFS Strategy % better HFd/HFu

    Mean HFd/HFu Max Half-Swarm 100 2.4 5.01 No-Supressor 100 2.56 4.44 Trigger 100 2.8 7.87
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    Evaluation of Directed Swarm Testing GCC Strategy % better HFd/HFu

    Mean HFd/HFu Max Half-Swarm 99 3.56 6.33 No-Supressor 94 3.03 5.58 Trigger 92 3.94 5.29
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    Evaluation of Directed Swarm Testing Spidermonkey Strategy % better HFd/HFu

    Mean HFd/HFu Max Half-Swarm 73 1.75 4.39 No-Supressor 65 1.15 3.14 Trigger 84 4.56 8.82
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    Evaluation of Directed Swarm Testing Multi-Target

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    Ability to find real faults Test Strategy Fault #1 Fault

    #2 Fault #3 Fault #4 Fault #5 Undirected Swarm 13.6 0.07 0.24 0.26 0.07 Round-robin Half-swarm 31.9 0.19 0.35 0.56 0.29 Round-robin No- suppressors 34.2 0.26 0.17 0.46 0.69 Subsumption Half-swarm 33.0 0.24 0.12 0.10 0.29 Subsumption No- suppressors 33.1 0.31 0.29 0.31 0.46 Spidermonkey - 5 real faults, multi-target testing
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    Final note • Using collected statistics on code coverage and

    swarm testing, it is possible to produce focused random tests • The method is able to increase the frequency with which tests cover targeted code by a factor often more than 2x • This approach is readily applicable to existing, industrial-strength random testing tools for critical systems software.
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    None
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    None
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    Targeted Testing Dynamic Symbolic Execution Search based testing

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    Evaluation of Directed Swarm Testing Single- Target Multi- Target

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    None
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    Limitations Focus is on Full System, program or a module

    Does not naturally focus on any parts of the program Why we need focus?
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    Swarm testing Fuzzer = generator + features Example of feature

    - api calls, grammar terminal & production rules, c semantics Configuration of a test generator Pure Random testing: configuration = set of all features Swarm testing: configuration = each feature chosen with a coin toss Increases feature count per test and increases interaction
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    - You want to hammer out a piece of code

    as frequent as possible.