Patrick Lam, Viktor Kuncak, and Martin Rinard. On our experience with modular pluggable analyses. Technical Report 965, MIT CSAIL, September 2004.

We present a technique that enables the focused application of multiple analyses to different modules in the same program. Our research has two goals: 1) to address the scalability limitations of precise analyses by focusing the analysis on only those parts of the program that are relevant to the properties that the analysis is designed to verify, and 2) to enable the application of specialized analyses that verify properties of specific classes of data structures to programs that simultaneously manipulate several different kinds of data structures. In our approach, each module encapsulates a data structure and uses membership in abstract sets to characterize how objects participate in its data structure. Each analysis verifies that the implementation of the module 1) preserves important internal data structure representation invariants and 2) conforms to a specification that uses formulas in a set algebra to characterize the effects of operations on the data structure. The analyses use the common set abstraction to 1) characterize how objects participate in multiple data structures and to 2) enable the inter-analysis communication required to verify properties that depend on multiple modules analyzed by different analyses. We characterize the key soundness property that an analysis plugin must satisfy to successfully participate in our system and present several analysis plugins that satisfy this property: a flag plugin that analyzes modules in which abstract set membership is determined by a flag field in each object, and a graph types plugin that analyzes modules in which abstract set membership is determined by reachability properties of objects stored in tree-like data structures.

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