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 generic higher-order inductive predicates describing spatial relationships together with a method of synthesizing new parameterized spatial predicates which can be used in combination with the higher-order predicates. In order to evaluate the proposed approach for realistic programs we have performed experiments on examples drawn from device drivers: the analysis proved safety of the data structure manipulation of several routines belonging to an IEEE 1394 (firewire) driver, and also found several previously unknown memory safety bugs.

Abstract. Separation logic (SL) has gained widespread popularity because of its ability to succinctly express complex invariants of a program's heap configurations. Several specialized provers have been developed for decidable SL fragments. However, these provers cannot be easily extended or combined with solvers for other theories that are important in program verification, e.g., linear arithmetic. In this paper, we present a reduction of decidable SL fragments to a decidable first-order theory that fits well into the satisfiability modulo theories (SMT) framework. We show how to use this reduction to automate satisfiability, entailment, frame inference, and abduction problems for separation logic using SMT solvers. Our approach provides a simple method of integrating separation logic into existing verification tools that provide SMT backends, and an elegant way of combining SL fragments with other decidable first-order theories. We implemented this approach in a verification tool and applied it to heap-manipulating programs whose verification involves reasoning in theory combinations.

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Abstract. Identifying the cause of an error is often the most timeconsuming part in program debugging. Fault localization techniques can help to automate this task. Particularly promising are static proof-based techniques that rely on an encoding of error traces into trace formulas. By identifying irrelevant portions of the trace formula, the possible causes of the error can be isolated. One limitation of these approaches is that they do not take into account the control flow of the program and therefore miss common causes of errors, such as faulty branching conditions. This limitation is inherent to the way the error traces are encoded. In this paper, we present a new flow-sensitive encoding of error traces into trace formulas. The new encoding enables proof-based techniques to identify irrelevant conditional choices in an error trace and to include a justification for the truth value of branching conditions that are relevant for the localized cause of an error. We apply our new encoding to the fault localization technique based on error invariants and show that it produces more meaningful error explanations than previous approaches.

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