1. The In Vivo Testing Approach Christian Murphy, Gail Kaiser, Ian Vo, Matt Chu Columbia University

2. 2 Chris Murphy, Columbia University Problem Statement It is infeasible to fully test a large system prior to deployment considering:  different runtime environments  different configuration options  different patterns of usage This problem may be compounded by moving apps from single-CPU machines to multi-core processors

3. 3 Chris Murphy, Columbia University Our Solution Continually test applications executing in the field (in vivo) as opposed to only testing in the development environment (in vitro) Conduct the tests in the context of the running application Do so without affecting the system’s users

4. 4 Chris Murphy, Columbia University int main ( ) {... foo(x); int main ( ) {... foo(x); test_foo(x); }...

5. 5 Chris Murphy, Columbia University Contributions A new testing approach called in vivo testing designed to execute tests in the deployment environment A new type of tests called in vivo tests An implementation framework called Invite

6. 6 Chris Murphy, Columbia University Related Work Perpetual testing [Clarke SAS’00] Skoll [Memon ICSE’04] Gamma [Orso ISSTA’02] CBI [Liblit PLDI’03] Distributed In Vivo Testing [Chu ICST’08]

7. 7 Chris Murphy, Columbia University Example of Defect: Cache private int numItems = 0, currSize = 0; private int maxCapacity = 1024; // in bytes public int getNumItems() { return numItems; } public boolean addItem(CacheItem i) throws... { numItems++; add(i); currSize += i.size; return true; } if (currSize + i.size < maxCapacity) { } else { return false; } Should only be incremented within “if” block Number of items in the cache Their size (in bytes) Maximum capacity

8. 8 Chris Murphy, Columbia University Insufficient Unit Test public void testAddItem() { Cache c = new Cache(); assert(c.addItem(new CacheItem())) assert(c.getNumItems() == 1); assert(c.addItem(new CacheItem())) assert(c.getNumItems() == 2); } 1. Assumes an empty/new cache 2. Doesn’t take into account various states that the cache can be in

9. 9 Chris Murphy, Columbia University Defects Targeted 1. Unit tests that make incomplete assumptions about the state of objects in the application 2. Possible field configurations that were not tested in the lab 3. A legal user action that puts the system in an unexpected state 4. A sequence of unanticipated user actions that breaks the system 5. Defects that only appear intermittently

10. 10 Chris Murphy, Columbia University Applications Targeted Applications that produce calculations or results that may not be obviously wrong  “Non-testable programs”  Simulations Applications in which exta-functional behavior may be wrong even if output is correct  Caching systems  Scheduling of tasks

11. 11 Chris Murphy, Columbia University In Vivo Testing: Process 1. Create test code (using existing unit tests or new In Vivo tests) 2. Instrument application using Invite testing framework 3. Configure framework 4. Deploy/execute application in the field

12. 12 Chris Murphy, Columbia University Model of Execution Function is about to be executed NO Execute function Yes Run a test? Create sandbox Run test Fork Stop Rest of program continues

13. 13 Chris Murphy, Columbia University Writing In Vivo Tests /* Method to be tested */ public boolean addItem(CacheItem i) {... } /* JUnit style test */ public void testAddItem() { Cache c = new Cache(); if (c.addItem(new CacheItem())) assert (c.getNumItems() == 1); } CacheItem i) { int oldNumItems = getNumItems(); this; boolean In Vivo returnoldNumItems+1; else return true; i))

14. 14 Chris Murphy, Columbia University Instrumentation /* Method to be tested */ public boolean __addItem(CacheItem i) {... } /* In Vivo style test */ public boolean testAddItem(CacheItem i) {... } public boolean addItem(CacheItem i) { if (Invite.runTest(“Cache.addItem”)) { Invite.createSandboxAndFork(); if (Invite.isTestProcess()) { if (testAddItem(i) == false) Invite.fail(); else Invite.succeed(); Invite.destroySandboxAndExit(); } return __addItem(i); }

15. 15 Chris Murphy, Columbia University Configuration Each instrumented method has a set probability ρ with which its test(s) will run To avoid bottlenecks, can also configure:  Maximum allowed performance overhead  Maximum number of simultaneous tests Also, what action to take when a test fails

16. 16 Chris Murphy, Columbia University Case Studies Applied testing approach to two caching systems  OSCache 2.1.1  Apache JCS 1.3 Both had known defects that were found by users (no corresponding unit tests for these defects) Goal: demonstrate that “traditional” unit tests would miss these but In Vivo testing would detect them

17. 17 Chris Murphy, Columbia University Experimental Setup An undergraduate student created unit tests for the methods that contained the defects These tests passed in “development” Student was then asked to convert the unit tests to In Vivo tests Driver created to simulate real usage in a “deployment environment”

18. 18 Chris Murphy, Columbia University Discussion In Vivo testing revealed all defects, even though unit testing did not Some defects only appeared in certain states, e.g. when the cache was at full capacity  These are the very types of defects that In Vivo testing is targeted at However, the approach depends heavily on the quality of the tests themselves

19. 19 Chris Murphy, Columbia University Performance Evaluation We instrumented three C and two Java applications with the framework and varied the value ρ (probability that a test is run) Applications were run with real-world inputs on a dual-core 3GHz server with 1GB RAM No restraints were placed on maximum allowable overhead or simultaneous tests

20. 20 Chris Murphy, Columbia University Experimental Results 0% 25% 50% 75% 100% percent of function calls resulting in tests Time (seconds)

21. 21 Chris Murphy, Columbia University Discussion Percent overhead is not a meaningful metric since it depends on the number of tests run  More tests = more overhead  Short-running programs with lots of tests will have significantly more “overhead” than long- running programs For C, the overhead was 1.5ms per test For Java, around 5.5ms per test

22. 22 Chris Murphy, Columbia University Future Work Ensure that test does not affect the external system state (database, network, etc.) Adjust frequency of test execution based on context or resource availability (CPU usage, number of threads, etc.) Apply approach to certain domains, e.g. security testing

23. 23 Chris Murphy, Columbia University Conclusion We have presented a new testing approach called in vivo testing designed to execute tests in the deployment environment We have also presented an implementation framework called Invite In Vivo testing is an effective technique at detecting defects not caught in the lab

24. The In Vivo Testing Approach Christian Murphy, Gail Kaiser, Ian Vo, Matt Chu Columbia University

25. 25 Chris Murphy, Columbia University Distributed In Vivo Testing [Chu ICST’08] Testing load is distributed to members of an “application community” Each of the N members perform 1/N th of the testing so as to reduce overhead We have also considered an “autonomic” approach that balances testing load according to usage profile