XSat: A Fast Floating-Point Satisfiability Solver

Fu, Zhoulai; Su, Zhendong

doi:10.1007/978-3-319-41540-6_11

Cited by 29 publications

(28 citation statements)

References 25 publications

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“…Combined with a finite search space for floating-point variables, this makes local-search and other "black-box" techniques an attractive proposition. XSat [28] was the first tool to directly make use of this approach (although Ariadne [5] could be seen as a partial precursor), making use of an external optimisation solver. goSAT [38] improved on this by compiling the formulae to an executable form.…”

Section: Techniquesmentioning

confidence: 99%

Building Better Bit-Blasting for Floating-Point Problems

Brain

Schanda

Sun

2019

Tools and Algorithms for the Construction and Analysis of Systems

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An effective approach to handling the theory of floatingpoint is to reduce it to the theory of bit-vectors. Implementing the required encodings is complex, error prone and requires a deep understanding of floating-point hardware. This paper presents SymFPU, a library of encodings that can be included in solvers. It also includes a verification argument for its correctness, and experimental results showing that its use in CVC4 out-performs all previous tools. As well as a significantly improved performance and correctness, it is hoped this will give a simple route to add support for the theory of floating-point.

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Section: Techniquesmentioning

confidence: 99%

Building Better Bit-Blasting for Floating-Point Problems

Brain

Schanda

Sun

2019

Tools and Algorithms for the Construction and Analysis of Systems

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“…Bit-blasting XSat [26] See text Mathematical optimisation Z3 [24] v4.6.0 Bit-blasting constraint language that only supports a subset of the semantics of the QF_FP theory. To allow a best-effort comparison with CORAL, we have implemented a tool to convert SMT-LIBv2.5 constraints into this language.…”

Section: Solver Configurationsmentioning

confidence: 99%

“…The goSAT [9] and XSat [26] solvers both reformulate finding a satisfying assignment as a mathematical optimisation problem and apply existing mathematical optimisation algorithms to try to find a global minimum. This is similar in spirit to JFS, FloPSy and CORAL in that the functions that goSAT and XSat seek to minimise are essentially fitness functions.…”

Section: Related Workmentioning

confidence: 99%

Just fuzz it: solving floating-point constraints using coverage-guided fuzzing

Liew

Cadar

Donaldson

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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We investigate the use of coverage-guided fuzzing as a means of proving satisfiability of SMT formulas over finite variable domains, with specific application to floating-point constraints. We show how an SMT formula can be encoded as a program containing a location that is reachable if and only if the program's input corresponds to a satisfying assignment to the formula. A coverage-guided fuzzer can then be used to search for an input that reaches the location, yielding a satisfying assignment. We have implemented this idea in a tool, Just Fuzz-it Solver (JFS), and we present a large experimental evaluation showing that JFS is both competitive with and complementary to state-of-the-art SMT solvers with respect to solving floating-point constraints, and that the coverage-guided approach of JFS provides significant benefit over naive fuzzing in the floating-point domain. Applied in a portfolio manner, the JFS approach thus has the potential to complement traditional SMT solvers for program analysis tasks that involve reasoning about floating-point constraints. CCS CONCEPTS • Theory of computation → Constraint and logic programming; • Software and its engineering → Software testing and debugging.

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“…We shall continue working on this topic, exploiting our expertise in logic (like in [3]) and also collaborating with machine learning and optimization techniques. A work related in essence is [31] for satisfiability check of assertions that involve floating-point operations.…”

Section: Testing Rather Than Verificationmentioning

confidence: 99%

Metamathematics for Systems Design

Hasuo

2017

New Gener. Comput.

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This position paper describes the context, the goal, the strategy and the tactics of the ERATO MMSD project (2016-2022. The project aims at enhanced quality assurance measures for industry products like cars. In doing so, we follow a recent trend and exploit formal methods, a body of mathematical techniques originally developed for computer systems. However, there are fundamental gaps in application of formal methods to industry products: additional concerns in industry products such as continuous dynamics of physical components and quantitative measures such as probability, time, and cost make problems fundamentally different from those about software. Formal methods that accommodate these concerns is an active research area, which shows that it is a hard problem. There are several successful theoretical developments in this direction. They typically combine one individual technique with one specific concern, such as hybrid automata that extend automata with continuous dynamics. Our project aims to contribute to this hard problem in a unique way. In our project we will take a unique metamathematical strategy to bridging the gaps: instead of creating one technique for each concern, we want to find a meta-level theory that describes how to develop such techniques for many potential concerns in general. Through this strategy, together with our emphasis on real-world applications in industry, we expect a new prototype of applied mathematics will emerge. In this prototype, abstraction and genericitycharacteristics of modern mathematics that are not often associated with application-are turned into crucial advantages in applications.

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XSat: A Fast Floating-Point Satisfiability Solver

Cited by 29 publications

References 25 publications

Building Better Bit-Blasting for Floating-Point Problems

Building Better Bit-Blasting for Floating-Point Problems

Just fuzz it: solving floating-point constraints using coverage-guided fuzzing

Metamathematics for Systems Design

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