Data flow refinement type inference
Data flow refinement type inference
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数据流细化类型推断
DOI:
10.1145/3434300
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发表时间:
2021
影响因子:
--
通讯作者:
Wies, Thomas
中科院分区:
文献类型:
--
作者:
Pavlinovic, Zvonimir;Su, Yusen;Wies, Thomas
Refinement types enable lightweight verification of functional programs. Algorithms for statically inferring refinement types typically work by reduction to solving systems of constrained Horn clauses extracted from typing derivations. An example is Liquid type inference, which solves the extracted constraints using predicate abstraction. However, the reduction to constraint solving in itself already signifies an abstraction of the program semantics that affects the precision of the overall static analysis. To better understand this issue, we study the type inference problem in its entirety through the lens of abstract interpretation. We propose a new refinement type system that is parametric with the choice of the abstract domain of type refinements as well as the degree to which it tracks context-sensitive control flow information. We then derive an accompanying parametric inference algorithm as an abstract interpretation of a novel data flow semantics of functional programs. We further show that the type system is sound and complete with respect to the constructed abstract semantics. Our theoretical development reveals the key abstraction steps inherent in refinement type inference algorithms. The trade-off between precision and efficiency of these abstraction steps is controlled by the parameters of the type system. Existing refinement type systems and their respective inference algorithms, such as Liquid types, are captured by concrete parameter instantiations. We have implemented our framework in a prototype tool and evaluated it for a range of new parameter instantiations (e.g., using octagons and polyhedra for expressing type refinements). The tool compares favorably against other existing tools. Our evaluation indicates that our approach can be used to systematically construct new refinement type inference algorithms that are both robust and precise.
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DOI:
10.1145/2908080.2908110
发表时间:
2016
期刊:
Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation
影响因子:
--
作者:
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DOI:
--
发表时间:
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期刊:
and Abstract Interpretation - VMCAI'18
影响因子:
--
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Kazerounian, Milod;Vazou, Niki;Bourgerie, Austin;Foster, Jeff;Torlak, Emina
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Torlak, Emina
DOI:
10.1007/978-3-642-18275-4_21
发表时间:
2011
期刊:
World's Poultry Science Journal
影响因子:
--
作者:
Se;K. Choe
通讯作者:
K. Choe
DOI:
10.1007/3-540-47813-2_16
发表时间:
2002
期刊:
World's Poultry Science Journal
影响因子:
--
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R. Gori;G. Levi
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G. Levi
DOI:
10.1007/978-3-642-02959-2_18
发表时间:
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期刊:
Cardiovascular revascularization medicine : including molecular interventions
影响因子:
--
作者:
Shuvendu K. Lahiri;S. Qadeer
通讯作者:
S. Qadeer