Quantitative dynamic‐memory analysis for Java

Space‐ and time‐predictability are hard to achieve for object‐oriented languages with automated dynamic‐memory management. Although there has been significant work to design APIs, such as the Real‐Time Specification for Java (RTSJ), and to implement garbage collectors to enable real‐time performance, quantitative space analysis is still in its infancy. This work presents the integration of a series of compile‐time analysis techniques to help predicting quantitative memory usage. In particular, we focus on providing tool assistance for identifying RTSJ scoped‐memory regions, their sizes, and overall memory usage. First, the tool‐suite synthesizes a memory organization where regions are associated with methods. Second, it infers their sizes in parametric closed form in terms of relevant program variables. Third, it exhibits a parametric upper bound on the amount of available free memory required to execute a method. The experiments carried out with a RTSJ benchmark, a real‐time aircraft collision detector, show that semi‐automatic, tool‐assisted generation of scoped‐based code is both helpful and doable.