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We present ShapeIt, a tool for mining specifications of cyber-physical systems (CPS) from their real-valued behaviors. The learned specifications are in the form of linear shape expressions, a declarative formal specification language suitable to express behavioral properties over real-valued signals. A linear shape expression is a regular expression composed of parameterized lines as atomic symbols with symbolic constraints on the line parameters. We present here the architecture of our tool along with the different steps of the specification mining algorithm. We also describe the usage of the tool demonstrating its applicability on several case studies from different application domains.

Cyber-physical systems (CPS) are increasingly becoming driven by data, using multiple types of sensors to capture huge amounts of data. Extraction and characterization of useful information from big streams of data is a challenging problem. Shape expressions facilitate formal specification of rich temporal patterns encountered in time series as well as in behaviors of CPS. In this paper, we introduce a method for systematically sampling shape expressions. The proposed approach combines methods for uniform sampling of automata (for exploring qualitative shapes) with hit-and-run Monte Carlo sampling procedures (for exploring multi-dimensional parameter spaces defined by sets of possibly non-linear constraints). We study and implement several possible solutions and evaluate them in the context of visualization and testing applications.

Debugging Cyber-Physical System (CPS) models can be extremely complex. Indeed, only the detection of a failure is insuffcient to know how to correct a faulty model. Faults can propagate in time and in space producing observable misbehaviours in locations completely different from the location of the fault. Understanding the reason of an observed failure is typically a challenging and laborious task left to the experience and domain knowledge of the designer. \n In this paper, we propose CPSDebug, a novel approach that by combining testing, specification mining, and failure analysis, can automatically explain failures in Simulink/Stateflow models. We evaluate CPSDebug on two case studies, involving two use scenarios and several classes of faults, demonstrating the potential value of our approach.

Model-based reasoning is a central concept in current research into intelligent diagnostic systems. It is based on the assumption that sources of incorrect behavior in technical devices can be located and identified via the existence of a model describing the basic properties of components of a certain application domain. When actual data concerning the misbehavior of a system composed from such components is available, a domain-independent diagnosis engine can be used to infer which parts of the system contribute to the observed behavior. This paper describes the application of the model-based approach to the debugging of Java programs written in a subset of Java. We show how a simple dependency model can be derived from a program, demonstrate the use of the model for debugging and reducing the required user interactions, give a comparison of the functional dependency model with program slicing, and finally discuss some current research issues., Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AADEBUG 2000), August 2000, Munich. cs.SE/0010035