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On Scalable Shape Analysis

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If you have a question about this talk, please contact Matthew Parkinson.

Shape analysis is a precise form of pointer analysis, which can be used to verify deep properties of data structures such as whether or not they are cyclic,whether they are nested, etc. Shape analyses are also expensive, and the tremendous number of abstract states they generate is an impediment to their use in verification of sizeable programs. In this talk, I will describe the techniques for improving the scalability of shape analyses. With these techniques, we have improved our analysis that was able to handle programs of up to 1,000 lines, such that it can now analyze programs of up to 10,000 lines. Our experiments also show that the new analysis is precise. It identifies memory safety errors and memory leaks in several Windows and Linux device drivers and, after these bugs are fixed, it automatically proves integrity of pointer manipulation for these drivers.

This order of magnitude improvement in sizes of programs verified is obtained by combining several ideas. One is the local reasoning idea of separation logic, which reduces recomputation of analysis of procedure bodies, and which allows efficient transfer functions for primitive program statements. Another is an interprocedural analysis algorithm which aggressively discards intermediate states. The most important new technical contribution of the work is a new join (or widening) operator, which greatly reduces the number of abstract states used by the analysis while not greatly reducing precision; the join is also integrated with procedure summaries in an interprocedural analysis.

This is joint work with Oukseh Lee, Cristiano Calcagno, Dino Distefano and Peter O’Hearn.

This talk is part of the Logic and Semantics Seminar (Computer Laboratory) series.

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