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In recent years, Satisfiability Modulo Theory (SMT) solvers are becoming increasingly popular in the Computer Aided Verification and Reasoning community. Used natively or as back-engines, they are accumulating a record of success stories and, as witnessed by the annual SMT competition, their performances and capacity are also increasing steadily. Introduced in previous contributions of ours, a new application domain providing an outstanding challenge for SMT solvers is represented by verification of Multi-Layer Perceptrons (MLPs) a widely-adopted kind of artificial neural network. In this paper we present an extensive evaluation of the current state-of-the-art SMT solvers and assess their potential in the promising domain of MLP verification. [ABSTRACT FROM AUTHOR]

A substantial effort is underway to make robots useful outside controlled environments and without direct human supervision. In practice, this can happen only after a rigorous certification process which ensures that several requirements, e.g., operation safety, are met. Automated verification of control programs can be part of cost-effective methodologies to support certification, but it is widely recognized as difficult to attain in robotics, because of several traits that characterize robot design and implementation. One such trait is the widespread usage of middleware to implement control programs in a distributed fashion. In these cases, the challenge of verification is made steep mainly by the fact that the correctness of control software depends on middleware components whose structured models might not be available, or too difficult to obtain from their documentation. Our proposal is to ease the application of automated verification techniques by identifying abstract middleware models in the form of finite-state automata. The identification procedure is itself largely automated, and the only prerequisite is for the middleware to be available for controlled experimentation. Once middleware models are computed, behaviors that would lead to unsafe operation can be spotted automatically on a composition of identified middleware and control software models using model checking techniques. The approach is based on our tool AIDE – Automata IDentification Engine – to identify abstract middleware models, and the model checker SPIN to verify control units. To validate our approach, we consider four different case studies built on YARP publish–subscribe middleware. Our results confirm that AIDE enables the extension of precise engineering methods to distributed control software in robotics. [ABSTRACT FROM AUTHOR]