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Monniaux_Menlo_Park_2011

Monniaux_Menlo_Park_2011 - Abstract interpretation David...

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. . . . . . Abstract interpretation David Monniaux CNRS / VERIMAG May 23–27, Menlo College David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 1 / 97
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. . . . . . Grenoble David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 2 / 97
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. . . . . . VERIMAG Joint lab between CNRS and Grenoble University 9 CNRS permanent researchers + 4 research engineers 23 professors David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 3 / 97
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. . . . . . . . . 1 Introduction Position within other techniques A short chronology Basic ideas . . . 2 Transition systems . . . 3 Boolean abstraction Definition Some more examples Abstraction refinement . . . 4 Intervals . . . 5 Extrapolation . . . 6 Executive summary David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 4 / 97
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. . . . . . Outline . . . 1 Introduction Position within other techniques A short chronology Basic ideas . . . 2 Transition systems . . . 3 Boolean abstraction Definition Some more examples Abstraction refinement . . . 4 Intervals . . . 5 Extrapolation . . . 6 Executive summary David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 5 / 97
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. . . . . . Static analysis Establish automatically that a program meets a specification . Specification can be: . . . 1 Explicit, e.g. “the program sorts the integer array given as input”. Can be expressed by e.g. temporal logics, assertions. . . . . . 2 Implicit, e.g. “the program never crashes due to division by zero, array overflow, bad pointer dereference”. Easier for the programmer (no need to write anything in addition to the code). David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 6 / 97
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. . . . . . Impossibilities Turing’s Halting Problem / Rice’s Theorem Program analysis is impossible unless one condition is met: . . . 1 Not fully automatic, requires user interaction. . . . 2 Constrained enough class of programs. . . . 3 Finite memory. . . . 4 Finite number of program steps. . . . 5 Analysis can answer false positives . . . . 6 Analysis can answer false negatives . David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 7 / 97
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. . . . . . User interaction Example: interactive theorem proving . Program analysis problems generally map to logics (e.g. Peano arithmetic) with no decision procedure. (Actually a way to prove undecidability of such logics. . . ) David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 8 / 97
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. . . . . . Finite memory Can enumerate reachable states explicitly. Computable but costly: n bits of memory in analyzed system 2 n states in analyzer David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 9 / 97
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. . . . . . Finite number of program steps Finite number of program steps + program statements with semantics in logics e.g. linear arithmetic Bounded model checking . David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 10 / 97
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. . . . . . Analysis can produce false negatives False negative = some bugs may be ignored Examples of techniques: testing Coverity David Monniaux (CNRS / VERIMAG) Abstract interpretation May 23–27, Menlo College 11 / 97
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  • Spring '11
  • MartinRinard
  • Abstraction, University of California, Santa Cruz, University of California, Santa Cruz colleges, Menlo College, College Ten, VERIMAG

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