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Faster Algorithms for Weighted Recursive State Machines

  • Krishnendu Chatterjee
  • Bernhard KraglEmail author
  • Samarth Mishra
  • Andreas Pavlogiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10201)

Abstract

Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entries and exits. We consider a general framework where RSM transitions are labeled from a semiring and path properties are algebraic with semiring operations, which can model, e.g., interprocedural reachability and dataflow analysis problems.

Our main contributions are new algorithms for several fundamental problems. As compared to a direct translation of RSMs to PDSs and the best-known existing bounds of PDSs, our analysis algorithm improves the complexity for finite-height semirings (that subsumes reachability and standard dataflow properties). We further consider the problem of extracting distance values from the representation structures computed by our algorithm, and give efficient algorithms that distinguish the complexity of a one-time preprocessing from the complexity of each individual query. Another advantage of our algorithm is that our improvements carry over to the concurrent setting, where we improve the best-known complexity for the context-bounded analysis of concurrent RSMs. Finally, we provide a prototype implementation that gives a significant speed-up on several benchmarks from the SLAM/SDV project.

Keywords

Node Distance Reachability Problem Distance Problem Exit Node Entry Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported in part by the Austrian Science Fund (FWF) under grants S11402-N23, S11407-N23, P23499-N23, and Z211-N23, and by the European Research Council (ERC) under grant 279307.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Krishnendu Chatterjee
    • 1
  • Bernhard Kragl
    • 1
    Email author
  • Samarth Mishra
    • 2
  • Andreas Pavlogiannis
    • 1
  1. 1.IST AustriaKlosterneuburgAustria
  2. 2.IIT BombayMumbaiIndia

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