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Symbolic Shape Analysis

Thomas Wies


The goal of program verification is to ensure software reliability by establishing a mathematical proof which guarantees that the software behaves correctly. Program analysis tools assist the developer in the verification process. Ideally a program analysis should be applicable to a wide range of verification problems without imposing a high burden on its users, i.e., without requiring deep mathematical knowledge and experience in program verification.

A big step forward towards this ideal has been achieved by combining abstract interpretation with techniques for automated reasoning. In abstract interpretation one transforms the concrete program into an abstract program. The abstract program enables the analysis to statically collect information over all possible executions of the concrete program. This information is used to automatically verify the correctness of the concrete program. Abstract interpretation increases the degree of automation in verification by shifting the burden of formally reasoning about programs from the developer to the designer of the program analysis. Automated reasoning pushes the degree of automation even further. It enables the automatic construction of the abstraction for a specific program and a specific correctness property and (if necessary) the automatic refinement of this abstraction. We refer to program analyses that combine abstract interpretation with automated reasoning as symbolic program analysis.

A problem that has recently seen much attention in program verification is the question of how to effectively deal with linked heap-allocated data structures. Program analyses that target properties of these data structures are commonly referred to as shape analyses. A symbolic shape analysis promises to handle a spectrum of different linked heap-allocated data structures, and a spectrum of properties to verify, without requiring the user to manually adjust the analysis to the specific problem instance. It was open what a symbolic shape analysis would look like. In this thesis we are concerned with this question.

We present domain predicate abstraction, which generalizes predicate abstraction to the point where it becomes effectively applicable for shape analysis. Domain predicate abstraction incorporates the key idea of three-valued shape analysis into predicate abstraction by replacing predicates on program states by predicates on objects in the heap of program states. We show how to automate the transformation of a heap-manipulating program into an abstract program using automated reasoning procedures. We further develop an abstraction refinement technique that complements domain predicate abstraction to a fully automated symbolic shape analysis. Finally, we present field constraint analysis, a new technique for reasoning about heap programs. Field constraint analysis enables the application of decision procedures for reasoning about specific data structures (such as trees) to arbitrary data structures. This technique makes our symbolic shape analysis applicable to the diverse data structures that occur in practice.

All the techniques presented in this thesis have been implemented and evaluated in the Bohne Verifier. We used Bohne to verify complex user-specified properties of data structure implementations. For instance, we were able to verify preservation of data structure invariants for operations on threaded binary trees (including sortedness and the in-order traversal invariant) without manually adjusting the analysis to this specific problem and without providing user assistance beyond stating the properties to verify. We are not aware of any other shape analysis that can verify such properties with a comparable degree of automation.


Publication typePhdThesis
InstitutionAlbert-Ludwigs-Universität Freiburg im Breisgau
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