Abstract Data refinement is a common approach to reasoning about programs, based on establishing that a concrete program indeed satisfies all the required properties imposed by an intended abstract pattern. Reasoning about programs in this setting becomes complex when use of pointers is assumed and, moreover, a well-known method for proving data refinement, namely the forward simulation method, becomes unsound in presence of pointers. The reason for unsoundness is the failure of the “lifting theorem” for simulations: that a simulation between abstract and concrete modules can be lifted to all client programs. The result is that simulation does not imply that a concrete can replace an abstract module in all contexts. Our diagnosis of this problem is that unsoundness is due to interference from the client programs. Rather than blame a module for the unsoundness of lifting simulations, our analysis places the blame on the client programs which cause the interference: when interference is not present, soundness is recovered. Technically, we present a novel instrumented semantics which is capable of detecting interference between a module and its client. With use of special simulation relations, namely growing relations, and interpreting the simulation method using the instrumented semantics, we obtain a lifting theorem. We then show situations under which simulation does indeed imply refinement.

We present a programming language, model, and logic appropriate for implementing and reasoning about a memory management system. We state semantically what is meant by correctness of a copying garbage collector, and employ a variant of the novel separation logics to formally specify partial correctness of Cheney's copying garbage collector in our program logic. Finally, we prove that our implementation of Cheney's algorithm meets its specification using the logic we have given and auxiliary variables.

We present a precise correspondence between separation logic and a simple notion of predicate BI, extending the earlier correspondence given between part of separation logic and propositional BI. Moreover, we introduce the notion of a BI hyperdoctrine, show that it soundly models classical and intuitionistic first- and higher-order predicate BI, and use it to show that we may easily extend separation logic to higher-order . We also demonstrate that this extension is important for program proving, since it provides sound reasoning principles for data abstraction in the presence of aliasing.

We show how to give a coherent semantics to programs that are well-specified in a version of separation logic for a language with higher types: idealized algol extended with heaps (but with immutable stack variables). In particular, we provide simple sound rules for deriving higher-order frame rules, allowing for local reasoning.

We show how to give a coherent semantics to programs that are well-specified in a version of separation logic for a language with higher types: idealized algol extended with heaps (but with immutable stack variables). In particular, we provide simple sound rules for deriving higher-order frame rules, allowing for local reasoning.

We present a programming language, model, and logic appropriate for implementing and reasoning about a memory management system. We then state what is meant by correctness of a copying garbage collector, and employ a variant of the novel separation logics [18, 23] to formally specify partial correctness of Cheney's copying garbage collector [8]. Finally, we prove that our implementation of Cheney's algorithm meets its specification, using the logic we have given, and auxiliary variables [19].