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We propose a shape analysis that adapts to some of the complex composite data structures found in industrial systems-level programs. Examples of such data structures include “cyclic doubly-linked lists of acyclic singly-linked lists”, “singly-linked lists of cyclic doublylinked lists with back-pointers to head nodes”, etc. The analysis introduces the use of(More)
Localizing the cause of an error in an error trace is one of the most time-consuming aspects of debugging. We develop a novel technique to automate this task. For this purpose, we introduce the concept of error invariants. An error invariant for a position in an error trace is a formula over program variables that over-approximates the reachable states at(More)
We show that the idea of predicates on heap objects can be cast in the framework of predicate abstraction. This leads to an alternative view on the underlying concepts of three-valued shape analysis by Sagiv, Reps and Wilhelm. Our construction of the abstract post operator is analogous to the corresponding construction for classical predicate abstraction,(More)
This paper presents our integration of efficient resolutionbased theorem provers into the Jahob data structure verification system. Our experimental results show that this approach enables Jahob to automatically verify the correctness of a range of complex dynamically instantiable data structures, such as hash tables and search trees, without the need for(More)
Automatic type inference is a popular feature of functional programming languages. If a program cannot be typed, the compiler typically reports a single program location in its error message. This location is the point where the type inference failed, but not necessarily the actual source of the error. Other potential error sources are not even considered.(More)
The problem of inferring an inductive invariant for verifying program safety can be formulated in terms of binary classification. This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets. Here, the good points are the reachable states(More)