Abstract. We present a type system for a compile-time analysis of heapspace requirements of Java style object-oriented programs with explicit deallocation. Our system is based on an amortised complexity analysis: the data is arbitrarily assigned a potential related to its size and layout; allocations must be ”payed for ” from this potential. The potential of each input then furnishes an upper bound on the heap space usage for the computation on this input. We successfully treat inheritance, downcast, update and aliasing. Example applications for the analysis include destination-passing style and doubly-linked lists. Type inference is explicitly not included; the contribution lies in the system elides most technical lemmas and proofs, even nontrivial ones, due to space limitations. A full version is available at the authors ’ web pages. 1

This work presents a technique to compute symbolic polynomial approximations of the amount of dynamic memory required to safely execute a method without running out of memory, for Javalike imperative programs. We consider object allocations and deallocations made by the method and the methods it transitively calls. More precisely, given an initial configuration of the stack and the heap, the peak memory consumption is the maximum space occupied by newly created objects in all states along a run from it. We over-approximate the peak memory consumption using a scopedmemory management where objects are organized in regions associated with the lifetime of methods. We model the problem of computing the maximum memory occupied by any region configuration as a parametric polynomial optimization problem over a polyhedral domain and resort to Bernstein basis to solve it. We apply the developed tool to several benchmarks.

Many software properties can be analysed through a relational size analysis on each function’s inputs and outputs. Such relational analysis (through a form of dependent typing) has been successfully applied to declarative programs, and to restricted imperative programs; but it has been elusive for object-based programs. The main challenge is that objects may mutate and they may be aliased. In this paper, we show how safety policies of programs can be analysed by tracking size properties of objects and be enforced by objects ’ invariants and the preconditions of methods. We propose several new ideas to allow both mutability and sharing of objects, whilst aiming for precision in our analysis. We introduce the concept of size-immutability to facilitate sharing, and also a set of alias controls to track unaliased objects whose size properties may change. We formalise our results through a set of advanced type checking rules for an object-based imperative language. We re-affirm the utility of the proposed type system by showing how a variety of software properties can be automatically verified according to size-inspired safety policies.

We consider a finitary procedural programming language (finite data-types, no recursion) extended with parallel composition and binary semaphores. Having first shown that may-equivalence of second-order open terms is undecidable we set out to find a framework in which decidability can be regained with minimum loss of expressivity. To that end we define an annotated type system that controls the number of concurrent threads created by terms and give a fully abstract game semantics for the notion of equivalence induced by typable terms and contexts. Finally, we show that the semantics of all typable terms, at any order and in the presence of iteration, has a regular-language representation and thus the restricted observational equivalence is decidable.

Abstract. This paper considers the use of dependent types to capture information about dynamic resource usage in a static type system. Dependent types allow us to give (explicit) proofs of properties with a program; we present a dependently typed core language ��, and define a framework within this language for representing size metrics and their properties. We give several examples of size bounded programs within this framework and show that we can construct proofs of their size bounds within ��. We further show how the framework handles recursive higher order functions and sum types, and contrast our system with previous work based on sized types. 1

This paper describes a general approach for optimized live heap space and live heap space-bound analyses for garbage-collected languages.

This paper describes a general approach for automatic and accurate space and space-bound analyses for high-level languages, considering stack space, heap allocation and live heap space usage of programs. The approach is based on program analysis and transformations and is fully automatic. The analyses produce accurate upper bounds in the presence of partially known input structures. The analyses have been implemented, and experimental results con rm the accuracy. 1

this article that directly depends on the locality restriction imposed on the -calculus.

This paper describes a general approach for automatic and accurate live heap space and live heap space-bound analyses for high-level languages. The approach is based on program analysis and transformations and is fully automatic. The space-bound analysis produces accurate (tight) upper bounds in the presence of partially known input structures. The analyses have been implemented and experimental results confirm their accuracy.

The use of types to deal with access capabilities of program entities is becoming increasingly popular.