Modular Synthesis of Sketches Using Models

One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular. The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.National Science Foundation (U.S.) (Grant NSF-1116362)National Science Foundation (U.S.) (Grant NSF-1139056)United States. Dept. of Energy (Grant DE-SC0005372

Multi-solver Support in Symbolic Execution

Abstract. One of the main challenges of dynamic symbolic execution— an automated program analysis technique which has been successfully employed to test a variety of software—is constraint solving. A key decision in the design of a symbolic execution tool is the choice of a constraint solver. While different solvers have different strengths, for most queries, it is not possible to tell in advance which solver will perform better. In this paper, we argue that symbolic execution tools can, and should, make use of multiple constraint solvers. These solvers can be run competitively in parallel, with the symbolic execution engine using the result from the best-performing solver. We present empirical data obtained by running the symbolic execution engine KLEE on a set of real programs, and use it to highlight several important characteristics of the constraint solving queries generated during symbolic execution. In particular, we show the importance of constraint caching and counterexample values on the (relative) performance of KLEE configured to use different SMT solvers. We have implemented multi-solver support in KLEE, using the metaSMT framework, and explored how different state-of-the-art solvers compare on a large set of constraint-solving queries. We also report on our ongoing experience building a parallel portfolio solver in KLEE.

Functional Analysis of Large-scale DNA Strand Displacement Circuits

Abstract. We present a method for the analysis of functional properties of large-scale DNA strand displacement (DSD) circuits based on Satisfiability Modulo Theories that enables us to prove the functional correctness of DNA circuit designs for arbitrary inputs, and provides significantly improved scalability and expressivity over existing methods. We implement this method as an extension to the Visual DSD tool, and use it to formalize the behavior of a 4-bit square root circuit, together with the components used for its construction. We show that our method successfully verifies that certain designs function as required and identifies erroneous computations in others, even when millions of copies of a circuit are interacting with each other in parallel. Our method is also applicable in the verification of properties for more general chemical reaction networks.