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Interprocedural optimization: eliminating unnecessary recompilation
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Efficient points-to analysis for whole-program analysis
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Which pointer analysis should I use?
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Practicing JUDO: Java under dynamic optimizations
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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
Modular interprocedural pointer analysis using access paths: design, implementation, and evaluation
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A framework for interprocedural optimization in the presence of dynamic class loading
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Effective synchronization removal for Java
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Adaptive optimization in the Jalapeño JVM
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Practical virtual method call resolution for Java
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Scalable propagation-based call graph construction algorithms
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Demand-driven pointer analysis
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Incrementalized pointer and escape analysis
Proceedings of the ACM SIGPLAN 2001 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
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SAS '02 Proceedings of the 9th International Symposium on Static Analysis
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Flow-Insensitive Points-to Analysis with Term and Set Constraints
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Effective interprocedural optimization of object-oriented languages
Connectivity-based garbage collection
OOPSLA '03 Proceedings of the 18th annual ACM SIGPLAN conference on Object-oriented programing, systems, languages, and applications
IBM Systems Journal
Escape analysis in the context of dynamic compilation and deoptimization
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Refinement-based context-sensitive points-to analysis for Java
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The java hotspotTM server compiler
JVM'01 Proceedings of the 2001 Symposium on JavaTM Virtual Machine Research and Technology Symposium - Volume 1
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JVM'01 Proceedings of the 2001 Symposium on JavaTM Virtual Machine Research and Technology Symposium - Volume 1
Precise analysis of string expressions
SAS'03 Proceedings of the 10th international conference on Static analysis
Client-driven pointer analysis
SAS'03 Proceedings of the 10th international conference on Static analysis
Dimensions of precision in reference analysis of object-oriented programming languages
CC'03 Proceedings of the 12th international conference on Compiler construction
Scaling Java points-to analysis using SPARK
CC'03 Proceedings of the 12th international conference on Compiler construction
APLAS'05 Proceedings of the Third Asian conference on Programming Languages and Systems
A study of type analysis for speculative method inlining in a JIT environment
CC'05 Proceedings of the 14th international conference on Compiler Construction
Using inter-procedural side-effect information in JIT optimizations
CC'05 Proceedings of the 14th international conference on Compiler Construction
Explaining failures of program analyses
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
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Journal of Systems and Software
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Pointer analysis benefits many useful clients, such as compiler optimizations and bug finding tools. Unfortunately, common programming language features such as dynamic loading, reflection, and foreign language interfaces, make pointer analysis difficult. This article describes how to deal with these features by performing pointer analysis online during program execution. For example, dynamic loading may load code that is not available for analysis before the program starts. Only an online analysis can analyze such code, and thus support clients that optimize or find bugs in it. This article identifies all problems in performing Andersen's pointer analysis for the full Java language, presents solutions to these problems, and uses a full implementation of the solutions in a Java virtual machine for validation and performance evaluation. Our analysis is fast: On average over our benchmark suite, if the analysis recomputes points-to results upon each program change, most analysis pauses take under 0.1 seconds, and add up to 64.5 seconds.