The undecidability of aliasing
ACM Transactions on Programming Languages and Systems (TOPLAS)
Points-to analysis in almost linear time
POPL '96 Proceedings of the 23rd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Precise flow-insensitive may-alias analysis is NP-hard
ACM Transactions on Programming Languages and Systems (TOPLAS)
Partial online cycle elimination in inclusion constraint graphs
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Unification-based pointer analysis with directional assignments
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Off-line variable substitution for scaling points-to analysis
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Scalable context-sensitive flow analysis using instantiation constraints
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Ultra-fast aliasing analysis using CLA: a million lines of C code in a second
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Pointer analysis: haven't we solved this problem yet?
PASTE '01 Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
An Efficient Inclusion-Based Points-To Analysis for Strictly-Typed Languages
SAS '02 Proceedings of the 9th International Symposium on Static Analysis
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Making context-sensitive points-to analysis with heap cloning practical for the real world
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Bootstrapping: a technique for scalable flow and context-sensitive pointer alias analysis
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Scalable Context-Sensitive Points-to Analysis Using Multi-dimensional Bloom Filters
APLAS '09 Proceedings of the 7th Asian Symposium on Programming Languages and Systems
Scaling Java points-to analysis using SPARK
CC'03 Proceedings of the 12th international conference on Compiler construction
Probabilistic points-to analysis
LCPC'01 Proceedings of the 14th international conference on Languages and compilers for parallel computing
Exploiting pointer and location equivalence to optimize pointer analysis
SAS'07 Proceedings of the 14th international conference on Static Analysis
Parallel replication-based points-to analysis
CC'12 Proceedings of the 21st international conference on Compiler Construction
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It has been established that achieving a points-to analysis that is scalable in terms of analysis time typically involves trading off analysis precsision and/or memory. In this paper, we propose a novel technique to approximate the solution of an inclusion-based points-to analysis. The technique is based on intelligently approximating pointer- and location-equivalence across variables in the program. We develop a simple approximation algorithm based on the technique. By exploiting various behavioral properties of the solution, we develop another improved algorithm which implements various optimizations related to the merging order, proximity search, lazy merging and identification frequency. The improved algorithm provides a strong control to the client to trade off analysis time and precision as per its requirements. Using a large suite of programs including SPEC 2000 benchmarks and five large open source programs, we show how our algorithm helps achieve a scalable solution.