Cloning-based context-sensitive pointer alias analysis using binary decision diagrams

This paper presents the first scalable context-sensitive, inclusionbased pointer alias analysis for Java programs. Our approach to context sensitivity is to create a clone of a method for every context of interest, and run a context-insensitive algorithm over the expanded call graph to get context-sensitive results. For precision, we generate a clone for every acyclic path through a program’s call graph, treating methods in a strongly connected component as a single node. Normally, this formulation is hopelessly intractable as a call graph often has 1014 acyclic paths or more. We show that these exponential relations can be computed efficiently using binary decision diagrams (BDDs). Key to the scalability of the technique is a context numbering scheme that exposes the commonalities across contexts. We applied our algorithm to the most popular applications available on Sourceforge, and found that the largest programs, with hundreds of thousands of Java bytecodes, can be analyzed in...

• Type: Conference
• Year: 2004
• Where: PLDI
• Authors: John Whaley, Monica S. Lam
• Keywords: Acyclic Paths, Binary Decision Diagrams, PLDI 2004, Pointer Alias Analysis, Programming Languages