On Our Experience with Modular Pluggable Analyses
Author(s)Lam, Patrick; Kuncak, Viktor; Rinard, Martin
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We present a technique that enables the focused applicationof multiple analyses to di erent modules in thesame program. In our approach, each module encapsulatesone or more data structures and uses membershipin abstract sets to characterize how objects participatein data structures. Each analysis veri es that the implementationof the module 1) preserves important internaldata structure consistency properties and 2) correctlyimplements an interface that uses formulas in a set algebrato characterize the e ects of operations on theencapsulated data structures. Collectively, the analysesuse the set algebra to 1) characterize how objects participatein multiple data structures and to 2) enable theinter-analysis communication required to verify propertiesthat depend on multiple modules analyzed by differentanalyses.We have implemented our system and deployed threepluggable analyses into it: a ag analysis for modulesin which abstract set membership is determined by aag eld in each object, a plugin for modules that encapsulatelinked data structures such as lists and trees,and an array plugin in which abstract set membershipis determined by membership in an array. Our experimentalresults indicate that our approach makes it possibleto e ectively combine multiple analyses to verifyproperties that involve objects shared by multiple modules,with each analysis analyzing only those modulesfor which it is appropriate.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory