Spatial Interpolants

We propose Splinter, a new technique for proving properties of heap-manipulating programs that marries (1) a new separation logic-based analysis for heap reasoning with (2) an interpolation-based technique for refining heap-shape invariants with data invariants. Splinter is property directed, precise, and produces counterexample traces when a property does not hold. Using the novel notion of spatial interpolants modulo theories, Splinter can infer complex invariants over general recursive predicates, e.g., of the form all elements in a linked list are even or a binary tree is sorted. Furthermore, we treat interpolation as a black box, which gives us the freedom to encode data manipulation in any suitable theory for a given program (e.g., bit vectors, arrays, or linear arithmetic), so that our technique immediately benefits from any future advances in SMT solving and interpolation.Comment: Short version published in ESOP 201

Propositional Reasoning about Safety and Termination of Heap-Manipulating Programs

This paper shows that it is possible to reason about the safety and termination of programs handling potentially cyclic, singly-linked lists using propositional reasoning even when the safety invariants and termination arguments depend on constraints over the lengths of lists. For this purpose, we propose the theory SLH of singly-linked lists with length, which is able to capture non-trivial interactions between shape and arithmetic. When using the theory of bit-vector arithmetic as a background, SLH is efficiently decidable via a reduction to SAT. We show the utility of SLH for software verification by using it to express safety invariants and termination arguments for programs manipulating potentially cyclic, singly-linked lists with unrestricted, unspecified sharing. We also provide an implementation of the decision procedure and use it to check safety and termination proofs for several heap-manipulating programs

Invariant Synthesis for Incomplete Verification Engines

We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counter-example guided inductive synthesis principle (CEGIS) and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs

Ripples in a pond: Do social work students need to learn about terrorism?

In the face of heightened awareness of terrorism, however it is defined, the challenges for social work are legion. Social work roles may include working with the military to ensure the well-being of service-men and women and their families when bereaved or injured, as well as being prepared to support the public within the emergency context of an overt act of terrorism. This paper reviews some of the literature concerning how social work responds to confl ict and terrorism before reporting a smallscale qualitative study examining the views of social work students, on a qualifying programme in the UK, of terrorism and the need for knowledge and understanding as part of their education