Verifying floating-point programs with constraint programming and abstract interpretation techniques

Static value analysis is a classical approach for verifying programs with floating-point computations. Value analysis mainly relies on abstract interpretation and over-approximates the possible values of program variables. State-of-the-art tools may however compute over-approximations that can be ra...

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Vydáno v:Automated software engineering Ročník 23; číslo 2; s. 191 - 217
Hlavní autoři: Ponsini, Olivier, Michel, Claude, Rueher, Michel
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2016
Springer Nature B.V
Springer Verlag
Témata:
Analyzers
Approximation
Artificial Intelligence
Computer Science
Embedded Systems
Floating point arithmetic
Programming
Software Engineering/Programming and Operating Systems
Solvers
Abstract interpretation-based approximation
Constraint solving over real number intervals
Program verification
Floating-point computation
Constraint solving over floating-point numbers
ISSN:0928-8910, 1573-7535
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Shrnutí:Static value analysis is a classical approach for verifying programs with floating-point computations. Value analysis mainly relies on abstract interpretation and over-approximates the possible values of program variables. State-of-the-art tools may however compute over-approximations that can be rather coarse for some very usual program expressions. In this paper, we show that constraint solvers can significantly refine approximations computed with abstract interpretation tools. More precisely, we introduce a hybrid approach combining abstract interpretation and constraint programming techniques in a single static and automatic analysis. This hybrid approach benefits from the strong points of abstract interpretation and constraint programming techniques, and thus, it is more effective than static analysers and constraint solvers, when used separately. We compared the efficiency of the system we developed—named rAiCp —with state-of-the-art static analyzers: rAiCp produces substantially more precise approximations and is able to check program properties on both academic and industrial benchmarks.
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ISSN:0928-8910
1573-7535
DOI:10.1007/s10515-014-0154-2