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RGITL: A temporal logic framework for compositional reasoning about interleaved programs.

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Title: RGITL: A temporal logic framework for compositional reasoning about interleaved programs.
Authors: Schellhorn, Gerhard, Tofan, Bogdan, Ernst, Gidon, Pfähler, Jörg, Reif, Wolfgang
Source: Annals of Mathematics & Artificial Intelligence; Jul2014, Vol. 71 Issue 1-3, p131-174, 44p
Subject Terms: REASONING, SEMANTICS, CALCULUS, MATHEMATICAL decomposition, ALGORITHMS
Abstract: This paper gives a self-contained presentation of the temporal logic Rely-Guarantee Interval Temporal Logic (RGITL). The logic is based on interval temporal logic (ITL) and higher-order logic. It extends ITL with explicit interleaved programs and recursive procedures. Deduction is based on the principles of symbolic execution and induction, known from the verification of sequential programs, which are transferred to a concurrent setting with temporal logic. We include an interleaving operator with compositional semantics. As a consequence, the calculus permits proving decomposition theorems which reduce reasoning about an interleaved program to reasoning about individual threads. A central instance of such theorems are rely-guarantee (RG) rules, which decompose global safety properties. We show how the correctness of such rules can be formally derived in the calculus. Decomposition theorems for other global properties are also derivable, as we show for the important progress property of lock-freedom. RGITL is implemented in the interactive verification environment KIV. It has been used to mechanize various proofs of concurrent algorithms, mainly in the area oflinearizable and lock-free algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Mathematics & Artificial Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: RGITL: A temporal logic framework for compositional reasoning about interleaved programs.
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  Data: Annals of Mathematics & Artificial Intelligence; Jul2014, Vol. 71 Issue 1-3, p131-174, 44p
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  Data: This paper gives a self-contained presentation of the temporal logic Rely-Guarantee Interval Temporal Logic (RGITL). The logic is based on interval temporal logic (ITL) and higher-order logic. It extends ITL with explicit interleaved programs and recursive procedures. Deduction is based on the principles of symbolic execution and induction, known from the verification of sequential programs, which are transferred to a concurrent setting with temporal logic. We include an interleaving operator with compositional semantics. As a consequence, the calculus permits proving decomposition theorems which reduce reasoning about an interleaved program to reasoning about individual threads. A central instance of such theorems are rely-guarantee (RG) rules, which decompose global safety properties. We show how the correctness of such rules can be formally derived in the calculus. Decomposition theorems for other global properties are also derivable, as we show for the important progress property of lock-freedom. RGITL is implemented in the interactive verification environment KIV. It has been used to mechanize various proofs of concurrent algorithms, mainly in the area oflinearizable and lock-free algorithms. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Annals of Mathematics & Artificial Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10472-013-9389-z
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        Text: English
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              Text: Jul2014
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