PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Accurate static estimators for program optimization
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
PLDI '96 Proceedings of the ACM SIGPLAN 1996 conference on Programming language design and implementation
Concurrent constraint programming: towards probabilistic abstract interpretation
Proceedings of the 2nd ACM SIGPLAN international conference on Principles and practice of declarative programming
Systematic design of program transformation frameworks by abstract interpretation
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Principles of Program Analysis
Principles of Program Analysis
Interprocedural Probabilistic Pointer Analysis
IEEE Transactions on Parallel and Distributed Systems
A probabilistic pointer analysis for speculative optimizations
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Abstract interpretation for worst and average case analysis
Program analysis and compilation, theory and practice
Relational Analysis and Precision via Probabilistic Abstract Interpretation
Electronic Notes in Theoretical Computer Science (ENTCS)
Probabilistic semantics and program analysis
SFM'10 Proceedings of the Formal methods for quantitative aspects of programming languages, and 10th international conference on School on formal methods for the design of computer, communication and software systems
Probabilistically accurate program transformations
SAS'11 Proceedings of the 18th international conference on Static analysis
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We present a formal framework for syntax directed probabilistic program analysis. Our focus is on probabilistic pointer analysis. We show how to obtain probabilistic points-to matrices and their relational counterparts in a systematic way via Probabilistic Abstract Interpretation (PAI). The analysis is based on a non-standard semantics for a simple imperative language which corresponds to a Discrete-Time Markov Chain (DTMC). The generator of this DTMC is constructed by composing (via tensor product) the probabilistic control flow of the program and the data updates of the different variables at individual program points. The dimensionality of the concrete semantics is in general prohibitively large but abstraction (via PAI) allows for a drastic (exponential) reduction of size.