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.

We present a static analysis for computing a parametric upper-bound of the amount of memory dynamically allocated by (Java-like) imperative object-oriented programs. We propose a general procedure for synthesizing non-linear formulas which conservatively estimate the quantity of memory explicitly allocated by a method as a function of its parameters. We have implemented the procedure and evaluated it on several benchmarks. Experimental results produced exact estimations for most test cases, and quite precise approximations for many of the others. We also apply our technique to compute usage in the context of scoped memory and discuss some open issues.

No abstract

Code artifacts that have nontrivial requirements with respect to the ordering in which their methods or procedures ought to be called are common and appear, for instance, in the form of API implementations and objects. This work addresses the problem of validating if API implementations provide their intended behavior when descriptions of this behavior are informal, partial, or nonexistent. The proposed approach addresses this problem by generating abstract behavior models which resemble typestates. These models are statically computed and encode all admissible sequences of method calls. The level of abstraction at which such models are constructed has shown to be useful for validating code artifacts and identifying findings which led to the discovery of bugs, adjustment of the requirements expected by the engineer to the requirements implicit in the code, and the improvement of available documentation.Fil: de Caso, Guido. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Braberman, Victor Adrian. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garbervetsky, Diego David. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Uchitel, Sebastian. Universidad de Buenos Aires; Argentina. Imperial College London; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Memory requirement estimation is an important issue in the development of embedded systems, since memory directly influences performance, cost and power consumption. It is therefore crucial to have tools that automatically compute accurate estimates of the memory requirements of programs to better control the development process and avoid some catastrophic execution exceptions. Many important memory issues can be expressed as the problem of maximizing a parametric polynomial defined over a parametric convex domain. Bernstein expansion is a technique that has been used to compute upper bounds on polynomials defined over intervals and parametric "boxes". In this paper, we propose an extension of this theory to more general parametric convex domains and illustrate its applicability to the resolution of memory issues with several application examples.

No abstract

No abstract

Modern development environments promote live programming (LP) mechanisms because it enhances the development experience by providing instantaneous feedback and interaction with live objects. LP is typically supported with advanced reflective techniques within dynamic languages. These languages run on top of Virtual Machines (VMs) that are built in a static manner so that most of their components are bound at compile time. As a consequence, VM developers are forced to work using the traditional edit-compile-run cycle, even when they are designing LP-supporting environments. In this paper we explore the idea of bringing LP techniques to the VM domain for improving their observability, evolution and adaptability at run-time. We define the notion of fully reflective execution environments (EEs), systems that provide reflection not only at the application level but also at the level of the VM. We characterize such systems, propose a design, and present Mate v1, a prototypical implementation. Based on our prototype, we analyze the feasibility and applicability of incorporating reflective capabilities into different parts of EEs. Furthermore, the evaluation demonstrates the opportunities such reflective capabilities provide for unanticipated dynamic adaptation scenarios, benefiting thus, a wider range of users.