30 research outputs found

    Time-Staging Enhancement of Hybrid System Falsification

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    Optimization-based falsification employs stochastic optimization algorithms to search for error input of hybrid systems. In this paper we introduce a simple idea to enhance falsification, namely time staging, that allows the time-causal structure of time-dependent signals to be exploited by the optimizers. Time staging consists of running a falsification solver multiple times, from one interval to another, incrementally constructing an input signal candidate. Our experiments show that time staging can dramatically increase performance in some realistic examples. We also present theoretical results that suggest the kinds of models and specifications for which time staging is likely to be effective

    Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

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    For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202

    Falsification of Signal-Based Specifications for Cyber-Physical Systems

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    In the development of software for modern Cyber-Physical Systems, testing is an integral part that is rightfully given a lot of attention. Testing is done on many different abstraction levels, and especially for large-scale industrial systems, it can be difficult to know when the testing should conclude and the software can be considered correct enough for making its way into production. This thesis proposes new methods for analyzing and generating test cases as a means of being more certain that proper testing has been performed for the system under test. For analysis, the proposed approach includes automatically finding how much a given test suite has executed the physical properties of the simulated system. For test case generation, an up-and-coming approach to find errors in Cyber-Physical Systems is simulation-based falsification. While falsification is suitable also for some large-scale industrial systems, sometimes there is a gap between what has been researched and what problems need to be solved to make the approach tractable in the industry. This thesis attempts to close this gap by applying falsification techniques to real-world models from Volvo Car Corporation, and adapting the falsification procedure where it has shortcomings for certain classes of systems. Specifically, the thesis includes a method for automatically transforming a signal-based specification into a formal specification in temporal logic, as well as a modification to the underlying optimization problem that makes falsification more viable in an industrial setting. The proposed methods have been evaluated for both academic benchmark examples and real-world industrial models. One of the main conclusions is that the proposed additions and changes to analysis and generation of tests can be useful, given that one has enough information about the system under test. It is difficult to provide a general solution that will always work best -- instead, the challenge lies in identifying which properties of the given system should be taken into account when trying to find potential errors in the system

    Signal Convolution Logic

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    We introduce a new logic called Signal Convolution Logic (SCL) that combines temporal logic with convolutional filters from digital signal processing. SCL enables to reason about the percentage of time a formula is satisfied in a bounded interval. We demonstrate that this new logic is a suitable formalism to effectively express non-functional requirements in Cyber-Physical Systems displaying noisy and irregular behaviours. We define both a qualitative and quantitative semantics for it, providing an efficient monitoring procedure. Finally, we prove SCL at work to monitor the artificial pancreas controllers that are employed to automate the delivery of insulin for patients with type-1 diabetes

    Robust Motion Planning employing Signal Temporal Logic

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    Motion planning classically concerns the problem of accomplishing a goal configuration while avoiding obstacles. However, the need for more sophisticated motion planning methodologies, taking temporal aspects into account, has emerged. To address this issue, temporal logics have recently been used to formulate such advanced specifications. This paper will consider Signal Temporal Logic in combination with Model Predictive Control. A robustness metric, called Discrete Average Space Robustness, is introduced and used to maximize the satisfaction of specifications which results in a natural robustness against noise. The comprised optimization problem is convex and formulated as a Linear Program.Comment: 6 page

    Combining Machine Learning and Formal Methods for Complex Systems Design

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    During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study.During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study

    On Optimization-Based Falsification of Cyber-Physical Systems

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    In what is commonly referred to as cyber-physical systems (CPSs), computational and physical resources are closely interconnected. An example is the closed-loop behavior of perception, planning, and control algorithms, executing on a computer and interacting with a physical environment. Many CPSs are safety-critical, and it is thus important to guarantee that they behave according to given specifications that define the correct behavior. CPS models typically include differential equations, state machines, and code written in general-purpose programming languages. This heterogeneity makes it generally not feasible to use analytical methods to evaluate the system’s correctness. Instead, model-based testing of a simulation of the system is more viable. Optimization-based falsification is an approach to, using a simulation model, automatically check for the existence of input signals that make the CPS violate given specifications. Quantitative semantics estimate how far the specification is from being violated for a given scenario. The decision variables in the optimization problems are parameters that determine the type and shape of generated input signals. This thesis contributes to the increased efficiency of optimization-based falsification in four ways. (i) A method for using multiple quantitative semantics during optimization-based falsification. (ii) A direct search approach, called line-search falsification that prioritizes extreme values, which are known to often falsify specifications, and has a good balance between exploration and exploitation of the parameter space. (iii) An adaptation of Bayesian optimization that allows for injecting prior knowledge and uses a special acquisition function for finding falsifying points rather than the global minima. (iv) An investigation of different input signal parameterizations and their coverability of the space and time and frequency domains. The proposed methods have been implemented and evaluated on standard falsification benchmark problems. Based on these empirical studies, we show the efficiency of the proposed methods. Taken together, the proposed methods are important contributions to the falsification of CPSs and in enabling a more efficient falsification process

    Enhancing Temporal Logic Falsification of Cyber-Physical Systems using multiple objective functions and a new optimization method

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    Cyber-physical systems (CPSs) are engineering systems that bridge the cyber-world of communications and computing with the physical world. These systems are usually safety-critical and exhibit both discrete and continuous dynamics that may have complex behavior. Typically, these systems have to satisfy given specifications, i.e., properties that define the valid behavior. One commonly used approach to evaluate the correctness of CPSs is testing. The main aim of testing is to detect if there are situations that may falsify the specifications.\ua0For many industrial applications, it is only possible to simulate the system under test because mathematical models do not exist, thus formal verification is not a viable option. Falsification is a strategy that can be used for testing CPSs as long as the system can be simulated and formal specifications exist. Falsification attempts to find counterexamples, in the form of input signals and parameters, that violate the specifications of the system. Random search or optimization can be used for the falsification process. In the case of an optimization-based approach, a quantitative semantics is needed to associate a simulation with a measure of the distance to a specification being falsified. This measure is used to guide the search in a direction that is more likely to falsify a specification, if possible. \ua0The measure can be defined in different ways. In this thesis, we evaluate different quantitative semantics that can be used to define this measure. The efficiency of the falsification can be affected by both the quantitative semantics used and the choice of the optimization method. The presented work attempts to improve the efficiency of the falsification process by suggesting to use multiple quantitative semantics, as well as a new optimization method. The use of different quantitative semantics and the new optimization method have been evaluated on standard benchmark problems. We show that the proposed methods improve the efficiency of the falsification process
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