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Selectively-Amortized Resource Bounding

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Tianhan Lu
Bor-Yuh Evan Chang
Ashutosh Trivedi
Published in
Static Analysis
Publisher
Springer International Publishing

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Abstract

The chapter 'Selectively-Amortized Resource Bounding' delves into the complex problem of proving bounds on resource usage in programs, particularly focusing on the challenge of resource-bound analysis. It introduces a novel framework that combines worst-case and fully amortized reasoning to provide a more flexible and precise method for bounding resource usage. The key insight is the decomposition of resource updates into amortized segments, allowing for selective application of amortized reasoning within specific segments of the program. This approach enables the use of both worst-case and amortized reasoning techniques, leveraging their strengths to improve the accuracy and efficiency of resource bounding. The chapter also includes a practical implementation of the selective amortization method, demonstrating its effectiveness through an empirical evaluation. This innovative method offers a significant advancement in the field of automated reasoning and resource bounding, making it a valuable read for specialists in computer science and software engineering.
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previous chapter Selective Context-Sensitivity for k-CFA with CFL-Reachability
next chapter Reduced Products of Abstract Domains for Fairness Certification of Neural Networks
Title
Selectively-Amortized Resource Bounding
Authors
Tianhan Lu
Bor-Yuh Evan Chang
Ashutosh Trivedi
Copyright Year
2021
Book
Static Analysis

Print ISBN: 978-3-030-88805-3

Electronic ISBN: 978-3-030-88806-0

Copyright Year: 2021

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