Points-to analysis with efficient strong updates
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Boosting the performance of flow-sensitive points-to analysis using value flow
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Reachability analysis of program variables
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Precise and scalable context-sensitive pointer analysis via value flow graph
Proceedings of the 2013 international symposium on memory management
Reachability analysis of program variables
ACM Transactions on Programming Languages and Systems (TOPLAS)
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Pointer analysis is a fundamental enabling technology for program analysis. By improving the scalability of precise pointer analysis we can make a positive impact across a wide range of program analyses used for many different purposes, including program verification and model checking, optimization and parallelization, program understanding, hardware synthesis, and more. In this thesis we present a suite of new algorithms aimed at improving pointer analysis scalability. These new algorithms make inclusion-based analysis (the most precise flow- and context-insensitive pointer analysis) over 4× faster while using 7× less memory than the previous state-of-the-art; they also enable flow-sensitive pointer analysis to handle programs with millions of lines of code, two orders of magnitude greater than the previous state-of-the-art. We present a formal framework for describing the space of pointer analysis approximations. The space of possible approximations is complex and multidimensional, and until now has not been well-defined in a formal manner. We believe that the framework is useful as a method to meaningfully compare the precision of the multitude of existing pointer analyses, as well as aiding in the systematic exploration of the entire space of approximations.