Abstract. Program synthesis is the mechanised construction of software. One of the main difficulties is the efficient exploration of the very large solution space, and tools often require a user-provided syntactic restriction of the search space. We propose a new approach to program synthesis that combines the strengths of a counterexample-guided inductive synthesizer with those of a theory solver, exploring the solution space more efficiently without relying on user guidance. We call this approach CEGIS(T ), where T is a first-order theory. In this paper, we focus on one particular challenge for program synthesizers, namely the generation of programs that require non-trivial constants. This is a fundamentally difficult task for state-of-the-art synthesizers. We present two exemplars, one based on Fourier-Motzkin (FM) variable elimination and one based on first-order satisfiability. We demonstrate the practical value of CEGIS(T ) by automatically synthesizing programs for a set of intricate benchmarks.

Modern control is implemented with digital microcontrollers, embedded within a dynamical plant that represents physical components. We present a new algorithm based on counterexample guided inductive synthesis that automates the design of digital controllers that are correct by construction. The synthesis result is sound with respect to the complete range of approximations, including time discretization, quantization effects, and finite-precision arithmetic and its rounding errors. We have implemented our new algorithm in a tool called DSSynth, and are able to automatically generate stable controllers for a set of intricate plant models taken from the literature within minutes.

We present a bounded model checking tool for verifying Java bytecode, which is built on top of the CPROVER framework, named Java Bounded Model Checker (JBMC). JBMC processes Java bytecode together with a model of the standard Java libraries and checks a set of desired properties. Experimental results show that JBMC can correctly verify a set of Java benchmarks from the literature and that it is competitive with two state-of-the-art Java verifiers. Support by ERC project 280053 CPROVER and the H2020 FET OPEN 712689 SC 2 .

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Static analysers search for overapproximating proofs of safety commonly known as safety invariants. Fundamentally, such analysers summarise traces into sets of states, thus trading the ability to distinguish traces for computational tractability. Conversely, static bug finders (e.g. Bounded Model Checking) give evidence for the failure of an assertion in the form of a counterexample, which can be inspected by the user. However, static bug finders fail to scale when analysing programs with bugs that require many iterations of a loop as the computational effort grows exponentially with the depth of the bug. We propose a novel approach for finding bugs, which delivers the performance of abstract interpretation together with the concrete precision of BMC. To do this, we introduce the concept of danger invariants -the dual to safety invariants. Danger invariants summarise sets of traces that are guaranteed to reach an error state. This summarisation allows us to find deep bugs without false alarms and without explicitly unwinding loops. We present a second-order formulation of danger invariants and use the solver described in [1] to compute danger invariants for intricate programs taken from the literature.