Undecidability of static analysis
ACM Letters on Programming Languages and Systems (LOPLAS)
Efficient context-sensitive pointer analysis for C programs
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
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)
Fast and accurate flow-insensitive points-to analysis
Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Partial online cycle elimination in inclusion constraint graphs
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Pointer analysis for programs with structures and casting
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Efficient points-to analysis for whole-program analysis
ESEC/FSE-7 Proceedings of the 7th European software engineering conference held jointly with the 7th ACM SIGSOFT international symposium on Foundations of software engineering
Projection merging: reducing redundancies in inclusion constraint graphs
Proceedings of the 27th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Introduction to set constraint-based program analysis
Science of Computer Programming
Which pointer analysis should I use?
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
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Off-line variable substitution for scaling points-to analysis
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
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PASTE '01 Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Extending and evaluating flow-insenstitive and context-insensitive points-to analyses for Java
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Points-to analysis for Java using annotated constraints
OOPSLA '01 Proceedings of the 16th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Polymorphic versus Monomorphic Flow-Insensitive Points-to Analysis for C
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
An Efficient Inclusion-Based Points-To Analysis for Strictly-Typed Languages
SAS '02 Proceedings of the 9th International Symposium on Static Analysis
Flow-Insensitive Points-to Analysis with Term and Set Constraints
Flow-Insensitive Points-to Analysis with Term and Set Constraints
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CC'03 Proceedings of the 12th international conference on Compiler construction
Importance of heap specialization in pointer analysis
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Online Cycle Detection and Difference Propagation: Applications to Pointer Analysis
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Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Efficient field-sensitive pointer analysis of C
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Evaluating the benefits of context-sensitive points-to analysis using a BDD-based implementation
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Semi-sparse flow-sensitive pointer analysis
Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Wave Propagation and Deep Propagation for Pointer Analysis
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
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SAS '09 Proceedings of the 16th International Symposium on Static Analysis
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Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
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The subject of this paper is flow- and context-insensitive pointer analysis. We present a novel approach for precisely modelling struct variables and indirect function calls. Our method emphasises efficiency and simplicity and extends the language of set-constraints. We experimentally evaluate the precision cost trade-off using a benchmark suite of 7 common C programs between 5,000 to 150,000 lines of code. Our results indicate the field-sensitive analysis is more expensive to compute, but yields significantly better precision.