We consider the problem of verification modulo tested library contracts as a step towards automating the verification of client programs that use complex libraries. We formulate this problem as the synthesis of modular contracts for the library methods used by the client that are adequate to prove the client correct, and that also pass the scrutiny of a testing engine that tests the library against these contracts. We also consider a new form of method contracts called contextual contracts that arise in this setting that hold in the context of the client program, and can often be simpler and easier to infer than classical modular contracts. We provide a counterexample-guided learning framework to solve this problem, in which the synthesizer interacts with a constraint solver as well as the testing engine in order to infer adequate modular/contextual method contracts and inductive invariants for the client. The main synthesis engines we use are generalizing CHC solvers that are realized using ICE learning algorithms. We realize this framework in a tool called Dualis and show its efficacy on benchmarks where clients call large libraries.

The executes-before relation between tasks is fundamental in the analysis of Event Driven Programs with several downstream applications like race detection and identifying redundant synchronizations. We present a sound, efficient, and effective static analysis technique to compute executes-before pairs of tasks for a general class of event driven programs. The analysis is based on a small but comprehensive set of rules evaluated on a novel structure called the task post graph of a program. We show how to use the executes-before information to identify disjoint-blocks in event driven programs and further use them to improve the precision of data race detection for these programs. We have implemented our analysis in the Flowdroid framework in a tool called AndRacer and evaluated it on several Android apps, bringing out the scalability, recall, and improved precision of the analyses