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Scaling Java Points-to Analysis Using SPARK

机译:使用SPARK扩展Java点分析

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Most points-to analysis research has been done on different systems by different groups, making it difficult to compare results, and to understand interactions between individual factors each group studied. Furthermore, points-to analysis for Java has been studied much less thoroughly than for C, and the tradeoffs appear very different. We introduce SPARK, a flexible framework for experimenting with points-to analyses for Java. SPARK supports equality- and subset-based analyses, variations in field sensitivity, respect for declared types, variations in call graph construction, off-line simplification, and several solving algorithms. SPARK is composed of building blocks on which new analyses can be based. We demonstrate SPARK in a substantial study of factors affecting precision and efficiency of subset-based points-to analyses, including interactions between these factors. Our results show that SPARK is not only flexible and modular, but also offers superior time/space performance when compared to other points-to analysis implementations.
机译:大多数要点分析研究已经通过不同的群体在不同的系统上完成,使得难以比较结果,并了解所研究各组的各个因素之间的相互作用。此外,对Java的点分析已经过分彻底地彻底研究,而权衡出现差异非常不同。我们介绍Spark,一个灵活的框架,用于试验Points-for Java分析。 Spark支持基于平等和基于子集的分析,场敏感性的变化,尊重声明的类型,呼叫图构造,离线简化和若干求解算法的变化。火花由建筑块组成,可以基于新分析。我们证明了对影响基于子集合点精度和效率的因素的大量研究中的激发,包括这些因素之间的相互作用。我们的结果表明,与其他要点相比,Spark不仅是灵活和模块化的,还提供卓越的时间/空间性能 - 分析实现。

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