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Public Deployment of Cooperative Bug Isolation

Public Deployment of Cooperative Bug Isolation. Ben Liblit , Mayur Naik, Alice Zheng, Alex Aiken, and Michael Jordan UC Berkeley, Stanford University and University of Wisconsin (pending). Our Goal: Measure Reality. We measure bridges, airplanes, cars…

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Public Deployment of Cooperative Bug Isolation

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  1. Public Deployment of Cooperative Bug Isolation Ben Liblit, Mayur Naik, Alice Zheng, Alex Aiken, and Michael Jordan UC Berkeley, Stanford University and University of Wisconsin (pending)

  2. Our Goal: Measure Reality • We measure bridges, airplanes, cars… • Where is ight data recorder for software? • Users are a vast, untapped resource • 96,000 new Kazaa users during this workshop • Users know what matters most • Opportunity for reality-directed debugging • Implicit bug triage for an imperfect world

  3.  € ƒ ‚ ƒ €  Bug Isolation Architecture Guesses ShippingApplication ProgramSource Sampler Compiler StatisticalDebugging Prole& J/L Top bugs withlikely causes

  4. Predicates on Program Behavior • Guess what might be interesting • Branches: Left? Right? • Function returns: Negative? Zero? Positive? • Pairs of variables: Less? Equal? Greater? • Reference counts: Alive? Dead? • Count how often guesses are true • Feedback: vector of counts + outcome label

  5. Sampling the Bernoulli Way • “Next sample” countdown • Geometric distribution • Split into acyclic regions • Finite threshold weight

  6. Sampling the Bernoulli Way • “Next sample” countdown • Geometric distribution • Split into acyclic regions • Finite threshold weight 4 3 1 2 1 2 1 1

  7. >4? Sampling the Bernoulli Way • “Next sample” countdown • Geometric distribution • Split into acyclic regions • Finite threshold weight • Clone acyclic regions • “Fast” & “slow” variants • Choose at run time • Result: • Subset of dynamic behavior • Statistically fair sample

  8. Multithreaded Programs • Global next-sample countdown • High contention, small footprint • Want to use registers for performance • Thread-local: one countdown per thread • Global predicate counters • Low contention, large footprint • Optimistic atomic increment

  9. Multi-Module Programs • Forget about global static analysis • Plug-ins, shared libraries • Instrumented & uninstrumented code • Self-management at compile time • Locally derive identifying object signature • Embed static site information within object le • Self-management at run time • Report feedback state on normal object unload • Signal handlers walk global object registry

  10. Guesses Sampler Compiler Native Compiler Integration • Instrumentor must mimic native compiler • You don’t have time to port & annotate by hand • Our approach: source-to-source, then native • Hooks for GCC: • Stage wrapping via scripts • Flag management via specles ShippingApplication ProgramSource CompilerTool Chain

  11. Keeping the User In Control

  12. Public Deployment, To Date

  13. Public Deployment, To Date

  14. Sneak Peak: Data Exploration

  15. Conclusions • Public deployment is challenging • Real world code pushes tools to their limits • Large user communities take time to build • But the results are worth it: “Thanks to Ben Liblit and the CooperativeBug Isolation Project, this version ofRhythmbox should be the most stable yet.”

  16. Join the Cause! The Cooperative Bug Isolation Project http://www.cs.berkeley.edu/~liblit/sampler/

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