Communications of the ACM
Control flow analysis in scheme
PLDI '88 Proceedings of the ACM SIGPLAN 1988 conference on Programming Language design and Implementation
Precise concrete type inference for object-oriented languages
OOPSLA '94 Proceedings of the ninth annual conference on Object-oriented programming systems, language, and applications
Demand-driven pointer analysis
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Automatic predicate abstraction of C programs
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
The SLAM project: debugging system software via static analysis
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Parametric shape analysis via 3-valued logic
ACM Transactions on Programming Languages and Systems (TOPLAS)
Parameterized object sensitivity for points-to and side-effect analyses for Java
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Machine Learning
Machine Learning
Solving Demand Versions of Interprocedural Analysis Problems
CC '94 Proceedings of the 5th International Conference on Compiler Construction
Construction of Abstract State Graphs with PVS
CAV '97 Proceedings of the 9th International Conference on Computer Aided Verification
Cloning-based context-sensitive pointer alias analysis using binary decision diagrams
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Parameterized object sensitivity for points-to analysis for Java
ACM Transactions on Software Engineering and Methodology (TOSEM)
Demand-driven points-to analysis for Java
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Effective static race detection for Java
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Refinement-based context-sensitive points-to analysis for Java
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Program analysis using random interpretation
Program analysis using random interpretation
Context-sensitive pointer analysis using binary decision diagrams
Context-sensitive pointer analysis using binary decision diagrams
Demand-driven alias analysis for C
Proceedings of the 35th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Evaluating the benefits of context-sensitive points-to analysis using a BDD-based implementation
ACM Transactions on Software Engineering and Methodology (TOSEM)
IEEE Transactions on Information Theory
Client-driven pointer analysis
SAS'03 Proceedings of the 10th international conference on Static analysis
Context-Sensitive points-to analysis: is it worth it?
CC'06 Proceedings of the 15th international conference on Compiler Construction
IEEE Transactions on Information Theory
Scaling abstraction refinement via pruning
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
POPL '12 Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Finding optimum abstractions in parametric dataflow analysis
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
Thresher: precise refutations for heap reachability
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
Bias-variance tradeoffs in program analysis
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Alias analysis for object-oriented programs
Aliasing in Object-Oriented Programming
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Static analyses are generally parametrized by an abstraction which is chosen from a family of abstractions. We are interested in flexible families of abstractions with many parameters, as these families can allow one to increase precision in ways tailored to the client without sacrificing scalability. For example, we consider k-limited points-to analyses where each call site and allocation site in a program can have a different k value. We then ask a natural question in this paper: What is the minimal (coarsest) abstraction in a given family which is able to prove a set of queries? In addressing this question, we make the following two contributions: (i) We introduce two machine learning algorithms for efficiently finding a minimal abstraction; and (ii) for a static race detector backed by a k-limited points-to analysis, we show empirically that minimal abstractions are actually quite coarse: It suffices to provide context/object sensitivity to a very small fraction (0.4-2.3%) of the sites to yield equally precise results as providing context/object sensitivity uniformly to all sites.