Models@runtime (models at runtime) are based on computation reflection. Runtime models can be regarded as a reflexive layer causally connected with the underlying system. Hence, every change in the runtime model involves a change in the reflected system, and vice versa. To the best of our knowledge, there are no runtime models for Python applications. Therefore, we propose a formal approach based on Petri Nets (PNs) to model, develop, and reconfigure Python applications at runtime. This framework is supported by a tool whose architecture consists of two modules connecting both the model and its execution. The proposed framework considers execution exceptions and allows users to monitor Python expressions at runtime. Additionally, the application behavior can be reconfigured by applying Graph Rewriting Rules (GRRs). A case study using Service-Level Agreement (SLA) violations is presented to illustrate our approach.

Developing High-confidence Cyber-Physical Production System (CPPS) is essential to ensure system efficacy and safety. Engineering requirements for CPPS is quite tricky because of system heterogeneity. Moreover, system bugs and malfunctions may occur, requiring a test to ensure the robustness of CPPSs. There are several kinds of CPPS test Methods in research. This work aims to categorize test methods of cyber-physical production systems in research. We follow a Systematic Literature Review (SLR) method to review existing research. We synthesize a dataset of 220 papers published from 2006 until 2021 seeking to survey and give structured research in the area of the CPPS test. We categorized CPPS test methods by presenting traditional methods of CPPS tests, such as formal techniques and simulation, in addition to the most used methods as testbed and containerization. We also presented used tools for testing CPPSs, and the objectives of testing CPPS.

Service composition is combining multiple services to provide for user query a new service which uses data from multiple service providers that are incorporated in the composition. In this situation, the data privacy and especially of the service providers can be breached. Therefore, keeping the data privacy during the composition process is crucial by every work in the context of the service composition. Recent approaches rely on a central mediator that can be trusted or not to ensuring the privacy of the service providers during the query execution. The most recent approaches found problems in case of untrusted mediator where they enforce restrictions that can affect the efficiency of their works. Therefore, we propose SCOPChain which preserves the privacy of data service providers during service composition using BlockChain technology. We used a permissioned BlockChain that acts as trusted mediator where it enables users to access to the BC if they get administrator permission. We use a BC framework called Hyperledger Fabric to implement our solution where it stores sensitive data about the composition whereat intermediate query results are saved in IPFS that acts as offchain storage. As a proof of concept, we have tested SCOPChain on a real-world medical dataset to show its feasibility and efficiency for maintaining privacy in a secure and trusted manner.

No abstract

No abstract