Polymorphic fractional capabilities

Hirotoshi Yasuoka, Tachio Terauchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The capability calculus is a framework for statically reasoning about program resources such as deallocatable memory regions. Fractional capabilities, originally proposed by Boyland for checking the determinism of parallel reads in multi-thread programs, extend the capability calculus by extending the capabilities to range over the rational numbers. Fractional capabilities have since found numerous applications, including race detection, buffer bound inference, security analyses, and separation logic. However, previous work on fractional capability systems either lacked polymorphism or lacked an efficient inference procedure. Automated inference is important for the application of the calculus to static analysis. This paper addresses the issue by presenting a polymorphic fractional capability calculus that allows polynomial-time inference via a reduction to rational linear programming.

Original languageEnglish
Title of host publicationStatic Analysis - 16th International Symposium, SAS 2009, Proceedings
Pages36-51
Number of pages16
DOIs
Publication statusPublished - 2009 Oct 29
Externally publishedYes
Event16th International Symposium on Static Analysis, SAS 2009 - Los Angeles, CA, United States
Duration: 2009 Aug 92009 Aug 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Symposium on Static Analysis, SAS 2009
CountryUnited States
CityLos Angeles, CA
Period09/8/909/8/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yasuoka, H., & Terauchi, T. (2009). Polymorphic fractional capabilities. In Static Analysis - 16th International Symposium, SAS 2009, Proceedings (pp. 36-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5673 LNCS). https://doi.org/10.1007/978-3-642-03237-0_5