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タイトル: ConSORT: Context- and Flow-Sensitive Ownership Refinement Types for Imperative Programs
著者: Toman, John
Siqi, Ren
Suenaga, Kohei  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-7466-8789 (unconfirmed)
Igarashi, Atsushi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-5143-9764 (unconfirmed)
Kobayashi, Naoki
著者名の別形: 末永, 幸平
五十嵐, 淳
小林, 直樹
キーワード: Refinement types
Mutable references
Aliasing
Strong updates
Fractional ownerships
Program verification
Type systems
発行日: 2020
出版者: Springer
誌名: Programming Languages and Systems
開始ページ: 684
終了ページ: 714
抄録: We present ConSORT, a type system for safety verification in the presence of mutability and aliasing. Mutability requires strong updates to model changing invariants during program execution, but aliasing between pointers makes it difficult to determine which invariants must be updated in response to mutation. Our type system addresses this difficulty with a novel combination of refinement types and fractional ownership types. Fractional ownership types provide flow-sensitive and precise aliasing information for reference variables. ConSORT interprets this ownership information to soundly handle strong updates of potentially aliased references. We have proved ConSORT sound and implemented a prototype, fully automated inference tool. We evaluated our tool and found it verifies non-trivial programs including data structure implementations.
記述: Programming Languages and Systems: 29th European Symposium on Programming, ESOP 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, 2020, Proceedings. Part of the Lecture Notes in Computer Science book series (LNCS, volume 12075). Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 12075).
著作権等: © The Author(s) 2020. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
URI: http://hdl.handle.net/2433/254111
DOI(出版社版): 10.1007/978-3-030-44914-8_25
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