Assume-Guarantee Testing

Verification techniques for component-based systems should ideally be able to predict properties of the assembled system through analysis of individual components before assembly. This work introduces such a modular technique in the context of testing. Assume-guarantee testing relies on the (automated) decomposition of key system-level requirements into local component requirements at design time. Developers can verify the local requirements by checking components in isolation; failed checks may indicate violations of system requirements, while valid traces from different components compose via the assume-guarantee proof rule to potentially provide system coverage. These local requirements also form the foundation of a technique for efficient predictive testing of assembled systems: given a correct system run, this technique can predict violations by alternative system runs without constructing those runs. We discuss the application of our approach to testing a multi-threaded NASA application, where we treat threads as components

Work-in-progress Assume-guarantee reasoning with ioco

This paper presents a combination between the assume-guarantee paradigm and the testing relation ioco. The assume-guarantee paradigm is a ”divide and conquer” technique that decomposes the verification of a system into smaller tasks that involve the verification of its components. The principal aspect of assume-guarantee reasoning is to consider each component separately, while taking into account assumptions about the context of the component. The testing relation ioco is a formal conformance relation for model-based testing that works on labeled transition systems. Our main result shows that, with certain restrictions, assume-guarantee reasoning can be applied in the context of ioco. This enables testing ioco-conformance of a system by testing its components separately

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficult as these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs) for visual perception. DNNs are hard to analyze due to their size (they can have thousands or millions of parameters), lack of formal specifications (DNNs are typically learnt from labeled data, in the absence of any formal requirements), and sensitivity to small changes in the environment. We present an assume-guarantee style compositional approach for the formal verification of system-level safety properties of such autonomous systems. Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties. The synthesized assumptions are the weakest in the sense that they characterize the output sequences of all the possible DNNs that, plugged into the autonomous system, guarantee the required safety properties. The assumptions can be leveraged as run-time monitors over a deployed DNN to guarantee the safety of the overall system; they can also be mined to extract local specifications for use during training and testing of DNNs. We illustrate our approach on a case study taken from the autonomous airplanes domain that uses a complex DNN for perception

Compositional Verification for Autonomous Systems with Deep Learning Components

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verification of autonomous systems that contain Deep Neural Network components. The approach uses assume-guarantee reasoning whereby {\em contracts}, encoding the input-output behavior of individual components, allow the designer to model and incorporate the behavior of the learning-enabled components working side-by-side with the other components. We illustrate the approach on an example taken from the autonomous vehicles domain