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International Static Analysis Symposium

SAS 2021: Static Analysis pp 286–307Cite as

Selectively-Amortized Resource Bounding

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 12913)

Abstract

We consider the problem of automatically proving resource bounds. That is, we study how to prove that an integer-valued resource variable is bounded by a given program expression. Automatic resource-bound analysis has recently received significant attention because of a number of important applications (e.g., detecting performance bugs, preventing algorithmic-complexity attacks, identifying side-channel vulnerabilities), where the focus has often been on developing precise amortized reasoning techniques to infer the most exact resource usage. While such innovations remain critical, we observe that fully precise amortization is not always necessary to prove a bound of interest. And in fact, by amortizing selectively, the needed supporting invariants can be simpler, making the invariant inference task more feasible and predictable. We present a framework for selectively-amortized analysis that mixes worst-case and amortized reasoning via a property decomposition and a program transformation. We show that proving bounds in any such decomposition yields a sound resource bound in the original program, and we give an algorithm for selecting a reasonable decomposition.

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We thank Pavol Černý for his valuable contributions in the early stages of this research. We also thank the anonymous reviewers and members of the CUPLV lab for their helpful reviews and suggestions. This research was supported in part by the Defense Advanced Research Projects Agency under grant FA8750-15-2-0096, and also by the National Science Foundation under grant CCF-2008369.

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Authors and Affiliations

  1. University of Colorado Boulder, Boulder, USA

    Tianhan Lu, Bor-Yuh Evan Chang & Ashutosh Trivedi

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  1. Tianhan Lu
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  2. Bor-Yuh Evan Chang
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  3. Ashutosh Trivedi
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Correspondence to Tianhan Lu , Bor-Yuh Evan Chang or Ashutosh Trivedi .

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Editors and Affiliations

  1. Informal Systems, Inria, ENS, DI - Ecole Normale Supérieure, Paris, France

    Cezara Drăgoi

  2. Microsoft Corporation, Redmond, WA, USA

    Suvam Mukherjee

  3. Nokia Bell Labs, Murray Hill, NJ, USA

    Dr. Kedar Namjoshi

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Lu, T., Chang, BY.E., Trivedi, A. (2021). Selectively-Amortized Resource Bounding. In: Drăgoi, C., Mukherjee, S., Namjoshi, K. (eds) Static Analysis. SAS 2021. Lecture Notes in Computer Science(), vol 12913. Springer, Cham. https://doi.org/10.1007/978-3-030-88806-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-88806-0_14

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