565 research outputs found
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a
wide range of verification and analysis tasks. Data-driven approaches show
great promise in improving CHC solving without the painstaking manual effort of
creating and tuning various heuristics. However, a large performance gap exists
between data-driven CHC solvers and symbolic reasoning-based solvers. In this
work, we develop a simple but effective framework, "Chronosymbolic Learning",
which unifies symbolic information and numerical data points to solve a CHC
system efficiently. We also present a simple instance of Chronosymbolic
Learning with a data-driven learner and a BMC-styled reasoner. Despite its
great simplicity, experimental results show the efficacy and robustness of our
tool. It outperforms state-of-the-art CHC solvers on a dataset consisting of
288 benchmarks, including many instances with non-linear integer arithmetics
Interaction effects between surface radiation and double-diffusive turbulent natural convection in an enclosed cavity filled with solid obstacles
The work reported here is a 2D numerical study on the buoyancy-driven low speed flow of humid air inside a rectangular cavity partially filled with solid cylindrical objects and whose vertical walls are maintained at 1.2 and 21 oC. This is a case of double diffusion where both temperature and concentration gradients are significant. Detailed calculations were carried out and results compared with reliable data, with the aim of investigating the influence of surface emissivity on heat and moisture transport. The Rayleigh number of the fluid mixture (air and water vapour) based on the height of the vertical wall is found to be 1.45 x 109.
In the computations, turbulent fluxes of the momentum, heat and mass were modelled by low-Re (Launder-Sharma) k-ε eddy viscosity model. The effect of radiation has been found to be significant even for the moderate temperature difference of 19.8 oC between the hot and the cold walls with the humid air participating in the radiation heat transfer. Variations of average Nusselt number and buoyancy flux are analysed and profiles of turbulent quantities are studied in order to observe the net effect of the intensity of turbulence. It has been found that a change in surface emissivity influences the humidity distribution and heat transfer within the cavity. It was also observed that during natural convection process the air/water vapour combination results in an increase in the heat transfer as compared to pure natural convection. An increase in heat transfer is observed using thermo-physical materials of higher surface emissivity. It can thus be implied that with the appropriate choice of components, the fluid flow, heat and mass transfer due to natural convection can be increased passively
Global guidance for local generalization in model checking
SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for Linear Integer Arithmetic and Linear Rational Aritmetic and implement them on top of Spacer solver in Z3. Our empirical results show that GSpacer, Spacer extended with global guidance, is significantly more effective than both Spacer and sole global reasoning, and, furthermore, is insensitive to interpolation
ADCL: Acceleration Driven Clause Learning for Constrained Horn Clauses
Constrained Horn Clauses (CHCs) are often used in automated program
verification. Thus, techniques for (dis-)proving satisfiability of CHCs are a
very active field of research. On the other hand, acceleration techniques for
computing formulas that characterize the N-fold closure of loops have
successfully been used for static program analysis. We show how to use
acceleration to avoid repeated derivations with recursive CHCs in resolution
proofs, which reduces the length of the proofs drastically. This idea gives
rise to a novel calculus for (dis)proving satisfiability of CHCs, called
Acceleration Driven Clause Learning (ADCL). We implemented this new calculus in
our tool LoAT and evaluate it empirically in comparison to other
state-of-the-art tools
Global Guidance for Local Generalization in Model Checking
SMT-based model checkers, especially IC3-style ones, are currently the most
effective techniques for verification of infinite state systems. They infer
global inductive invariants via local reasoning about a single step of the
transition relation of a system, while employing SMT-based procedures, such as
interpolation, to mitigate the limitations of local reasoning and allow for
better generalization. Unfortunately, these mitigations intertwine model
checking with heuristics of the underlying SMT-solver, negatively affecting
stability of model checking. In this paper, we propose to tackle the
limitations of locality in a systematic manner. We introduce explicit global
guidance into the local reasoning performed by IC3-style algorithms. To this
end, we extend the SMT-IC3 paradigm with three novel rules, designed to
mitigate fundamental sources of failure that stem from locality. We instantiate
these rules for the theory of Linear Integer Arithmetic and implement them on
top of SPACER solver in Z3. Our empirical results show that GSPACER, SPACER
extended with global guidance, is significantly more effective than both SPACER
and sole global reasoning, and, furthermore, is insensitive to interpolation.Comment: Published in CAV 202
Global Guidance for Local Generalization in Model Checking
SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for the theory of Linear Integer Arithmetic and implement them on top of Spacer solver in Z3. Our empirical results show that GSpacer, Spacer extended with global guidance, is significantly more effective than both Spacer and sole global reasoning, and, furthermore, is insensitive to interpolation
- …