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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/9784

タイトル: Stacking-based context-sensitive points-to analysis for java
著者: Li, Xin
Ogawa, Mizuhito
キーワード: Points-to analaysis
context-sensitive
Java
Weighted pushdown model checking
発行日: 2009/10/20
出版者: Springer
誌名: Lecture Notes in Computer Science
巻: 6405/2011
開始ページ: 133
終了ページ: 149
DOI: 10.1007/978-3-642-19237-1_14
抄録: Points-to analysis for Java infers heap objects that a reference variable can point to. Existing practiced context-sensitive points-to analyses are cloning-based, with an inherent limit to handle recursive procedure calls and being hard to scale under deep cloning. This paper presents a stacking-based context-sensitive points-to analysis for Java, by deriving the analysis as weighted pushdown model checking problems. To generate a tractable model for model checking, instead of passing global variables as parameters along procedure calls and returns, we model the heap memory with a global data structure that stores and loads global references with synchronized points-to information on-demand. To accelerate the analysis, we propose a two-staged iterative procedure that combines local exploration for lightening most of iterations and global update for guaranteeing soundness. In particular, summary transition rules that carry cached data flows are carefully introduced to trigger each local exploration, which boosts the convergence with retaining the precision. Empirical studies show that, our analysis scales well to Java benchmarks of significant size, and achieved in average 2.5X speedup in the two-staged analysis framework.
Rights: This is the author-created version of Springer, Xin Li, Mizuhito Ogawa, Lecture Notes in Computer Science, 6405/2011, 2009, 133-149. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-19237-1_14
URI: http://hdl.handle.net/10119/9784
資料タイプ: author
出現コレクション:b10-1. 雑誌掲載論文 (Journal Articles)

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