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
Java Virtual Machine Specification
Java Virtual Machine Specification
Using benchmarking to advance research: a challenge to software engineering
Proceedings of the 25th International Conference on Software Engineering
Interprocedural side-effect analysis and optimisation in the presence of dynamic class loading
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Using Static Analysis to Reduce Dynamic Analysis Overhead
Formal Methods in System Design
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Platform-independent profiling in a virtual execution environment
Software—Practice & Experience
Fast and precise points-to analysis
Information and Software Technology
Towards Comparing and Combining Points-to Analyses
SCAM '09 Proceedings of the 2009 Ninth IEEE International Working Conference on Source Code Analysis and Manipulation
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
Taming reflection: Aiding static analysis in the presence of reflection and custom class loaders
Proceedings of the 33rd International Conference on Software Engineering
Dynamic anomaly detection for more trustworthy outsourced computation
ISC'12 Proceedings of the 15th international conference on Information Security
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Many dynamic analysis tools capture the occurrences of events at runtime. The longer programs are being monitored, the more accurate the data they provide to the user. Then, the runtime overhead must be kept as low as possible, because it decreases the user's productivity. Runtime performance overhead occurs due to identifying events, and storing them in a result data-structure. We address the latter issue by generating custom-made instrumentation code for each program. By using static analysis to get a priori knowledge about which events of interest can occur and where they can occur, tailored code for storing those events can be generated for each program. We evaluate our idea by comparing the runtime overhead of a "general purpose" dynamic analysis tool that captures points-to information for Java programs with approaches based on custom-made instrumentation code. Experiments suggest highly reduced performance overhead for the latter.