Probabilistic symbolic execution
Proceedings of the 2012 International Symposium on Software Testing and Analysis
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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. Copyright © 2010 John Wiley & Sons, Ltd.