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Lean is a new open source theorem prover being developed at Microsoft Research and Carnegie Mellon University, with a small trusted kernel based on dependent type theory. It aims to bridge the gap between interactive and automated theorem proving, by situating automated tools and methods in a framework that supports user interaction and the construction of… (More)

- Sicun Gao, Soonho Kong, Edmund M. Clarke
- CADE
- 2013

We describe the open-source tool dReal, an SMT solver for nonlinear formulas over the reals. The tool can handle various nonlinear real functions such as polynomials, trigonometric functions, exponential functions, etc. dReal implements the framework of δ-complete decision procedures: It returns either unsat or δ-sat on input formulas, where δ is a… (More)

- Sicun Gao, Soonho Kong, Edmund M. Clarke
- FMCAD
- 2013

—We study SMT problems over the reals containing ordinary differential equations,. They are important for formal verification of realistic hybrid systems and embedded software. We develop δ-complete algorithms for SMT formulas that are purely existentially quantified, as well as ∃∀-formulas whose universal quantification is restricted to the time variables.… (More)

- Yungbum Jung, Soonho Kong, Bow-Yaw Wang, Kwangkeun Yi
- VMCAI
- 2010

By combining algorithmic learning, decision procedures, and predicate abstraction, we present an automated technique for finding loop invariants in propositional formulae. Given invariant approximations derived from pre-and post-conditions, our new technique exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is… (More)

- Soonho Kong, Sicun Gao, Wei Chen, Edmund M. Clarke
- TACAS
- 2015

dReach is a bounded reachability analysis tool for nonlinear hybrid systems. It encodes reachability problems of hybrid systems to first-order formulas over real numbers, which are solved by delta-decision procedures in the SMT solver dReal. In this way, dReach is able to handle a wide range of highly nonlinear hybrid systems. It has scaled well on various… (More)

- Soonho Kong, Yungbum Jung, Cristina David, Bow-Yaw Wang, Kwangkeun Yi
- APLAS
- 2010

By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invariants. Our technique can find arbitrary first-order invariants (modulo a fixed set of atomic propositions and an underlying SMT solver) in the form of the given template and exploits the… (More)

- Sicun Gao, Soonho Kong, Wei Chen, Edmund M. Clarke
- ArXiv
- 2014

We present the framework of δ-complete analysis for bounded reachability problems of general hybrid systems. We perform bounded reachability checking through solving δ-decision problems over the reals. The techniques take into account of robustness properties of the systems under numerical perturbations. We prove that the verification problems become much… (More)

- Leonardo Mendonça de Moura, Jeremy Avigad, Soonho Kong, Cody Roux
- ArXiv
- 2015

To be usable in practice, interactive theorem provers need to provide convenient and efficient means of writing expressions, definitions, and proofs. This involves inferring information that is often left implicit in an ordinary mathematical text, and resolving ambiguities in mathematical expressions. We refer to the process of passing from a quasi-formal… (More)

- Soonho Kong, Nikolai Tillmann, Jonathan de Halleux
- 2009 Sixth International Conference on…
- 2009

Programs that interact with the file system are a classical challenge for automated software testing. A common approach to handling this problem is to insert an abstraction layer between the application and the file system. However, even with a well-defined abstraction layer, the burden on the software developer or tester is still high: they have to… (More)

- Qinsi Wang, Paolo Zuliani, Soonho Kong, Sicun Gao, Edmund M. Clarke
- ArXiv
- 2014

We present a novel approach for solving the probabilistic bounded reachability problem of hybrid systems with parameter uncertainty. Standard approaches to this problem require numerical solutions for large optimization problems, and become unfeasible for systems involving nonlinear dynamics over the reals. Our approach combines randomized sampling of… (More)