No abstract

This report presents the results from the 2019 friendly competition in the ARCH workshop for the falsification of temporal logic specifications over Cyber-Physical Systems. We describe the organization of the competition and how it differs from previous years. We give background on the participating teams and tools and discuss the selected benchmarks and results. The benchmarks are available on the ARCH website1, as well as in the competition’s gitlab repository2. The main outcome of the 2019 competition is a common benchmark repository, and an initial base-line for falsification, with results from multiple tools, which will facilitate comparisons and tracking of the state-of-the-art in falsification in the future.

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification-a methodology of search-based testing that employs stochastic optimization-is thus attracting attention as an alternative quality assurance method. Inspired by the recent work that advocates coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations (e.g. in computer Go). MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner. We demonstrate the proposed framework through experiments with benchmarks from the automotive domain.

This paper presents a model‐driven software process suitable to develop a set of integrated tools around a formal method. This process exploits concepts and technologies of the Model‐driven Engineering (MDE) approach, such as metamodelling and automatic generation of software artifacts from models. We describe the requirements to fulfill and the development steps of this model‐driven process. As a proof‐of‐concept, we apply it to the Finite State Machines and we report our experience in engineering a metamodel‐based language and a toolset for the Abstract State Machine formal method. Copyright © 2011 John Wiley & Sons, Ltd.

Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded. This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines , that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.

No abstract

Abstract. We present CoMA (Conformance Monitoring by Abstract State Machines), a specification-based approach and its supporting tool for runtime monitoring of Java software. Based on the information obtained from code execution and model simulation, the conformance of the concrete implementation is checked with respect to its formal specification given in terms of Abstract State Machines. At runtime, undesirable behaviors of the implementation, as well as incorrect specifications of the system behavior are recognized. The technique we propose makes use of Java annotations, which link the concrete implementation to its formal model, without enriching the code with behavioral information contained only in the abstract specification. The approach fosters the separation between implementation and specification, and allows the reuse of specifications for other purposes (formal verification, simulation, model-based testing, etc.).

No abstract