First order logic with transitive closure, and separation logic enable elegant interactive verification of heap-manipulating programs. However, undecidabilty results and high asymptotic complexity of checking validity preclude complete automatic verification of such programs, even when loop invariants and procedure contracts are specified as formulas in these logics. This paper tackles the problem of procedure-modular verification of reachability properties of heap-manipulating programs using efficient decision procedures that are complete: that is, a SAT solver must generate a counterexample whenever a program does not satisfy its specification. By (a) requiring each procedure modifies a fixed set of heap partitions and creates a bounded amount of heap sharing, and (b) restricting program contracts and loop invariants to use only deterministic paths in the heap, we show that heap reachability updates can be described in a simple manner. The restrictions force program specifications and verification conditions to lie within a fragment of first-order logic with transitive closure that is reducible to effectively propositional logic, and hence facilitate sound, complete and efficient verification. We implemented a tool atop Z3 and report on preliminary experiments that establish the correctness of several programs that manipulate linked data structures.

Given a high-level specification and a low-level programming language, our goal is to automatically synthesize an efficient program that meets the specification. In this paper, we present a new algorithmic methodology for inductive synthesis that allows us to do this.We use Second Order logic as our generic high level specification logic. For our low-level languages we choose small application-specific logics that can be immediately translated into code that runs in expected linear time in the worst case.We explain our methodology and provide examples of the synthesis of several graph classifiers, e.g, linear-time tests of whether the input graph is connected, acyclic, etc. In another set of applications we automatically derive many finite differencing expressions equivalent to ones that Paige built by hand in his thesis [Pai81]. Finally we describe directions for automatically combining such automatically generated building blocks to synthesize efficient code implementing more complicated specifications.The methods in this paper have been implemented in Python using the SMT solver Z3 [dMB].