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A Transformational Approach to Resource Analysis with Typed-norms Inference
Published online by Cambridge University Press: 05 September 2019
Abstract
In order to automatically infer the resource consumption of programs, analyzers track how data sizes change along program’s execution. Typically, analyzers measure the sizes of data by applying norms which are mappings from data to natural numbers that represent the sizes of the corresponding data. When norms are defined by taking type information into account, they are named typed-norms. This article presents a transformational approach to resource analysis with typed-norms that are inferred by a data-flow analysis. The analysis is based on a transformation of the program into an intermediate abstract program in which each variable is abstracted with respect to all considered norms which are valid for its type. We also present the data-flow analysis to automatically infer the required, useful, typed-norms from programs. Our analysis is formalized on a simple rule-based representation to which programs written in different programming paradigms (e.g., functional, logic, and imperative) can be automatically translated. Experimental results on standard benchmarks used by other type-based analyzers show that our approach is both efficient and accurate in practice.
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- Copyright © Cambridge University Press 2019
Footnotes
This work was funded partially by the Spanish MICINN/FEDER, UE projects RTI2018-094403-B-C31 and RTI2018-094403-B-C32, MINECO projects TIN2015-69175-C4-2-R and TIN2015-69175-C4-1-R, by the CM project S2018/TCS-4314, the GV project PROMETEO/2019/098, and the UPV project SP20180225. Raúl Gutiérrez was also supported by INCIBE program “Ayudas para la excelencia de los equipos de investigación avanzada en ciberseguridad.”
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