A Formal Methodology For The Verfication Of Concurrent Systems
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A Formal Methodology for the Verification of Concurrent Systems
There is an increasing emphasis on the use of software to control safety critical plants for a wide area of applications. The importance of ensuring the correct operation of such potentially hazardous systems points to an emphasis on the verification of the system relative to a suitably secure specification. However, the process of verification is often made more complex by the concurrency and real-time considerations which are inherent in many applications. A response to this is the use of formal methods for the specification and verification of safety critical control systems. These provide a mathematical representation of a system which permits reasoning about its properties. This thesis investigates the use of the formal method Communicating Sequential Processes (CSP) for the verification of a safety critical control application. CSP is a discrete event based process algebra which has a compositional axiomatic semantics that supports verification by formal proof. The application is an industrial case study which concerns the concurrent control of a real-time high speed mechanism. It is seen from the case study that the axiomatic verification method employed is complex. It requires the user to have a relatively comprehensive understanding of the nature of the proof system and the application. By making a series of observations the thesis notes that CSP possesses the scope to support a more procedural approach to verification in the form of testing. This thesis investigates the technique of testing and proposes the method of Ideal Test Sets. By exploiting the underlying structure of the CSP semantic model it is shown that for certain processes and specifications the obligation of verification can be reduced to that of testing the specification over a finite subset of the behaviours of the process.
Compositional Verification of Concurrent and Real-Time Systems
Author: Eric Y.T. Juan
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
With the rapid growth of networking and high-computing power, the demand for large-scale and complex software systems has increased dramatically. Many of the software systems support or supplant human control of safety-critical systems such as flight control systems, space shuttle control systems, aircraft avionics control systems, robotics, patient monitoring systems, nuclear power plant control systems, and so on. Failure of safety-critical systems could result in great disasters and loss of human life. Therefore, software used for safety critical systems should preserve high assurance properties. In order to comply with high assurance properties, a safety-critical system often shares resources between multiple concurrently active computing agents and must meet rigid real-time constraints. However, concurrency and timing constraints make the development of a safety-critical system much more error prone and arduous. The correctness of software systems nowadays depends mainly on the work of testing and debugging. Testing and debugging involve the process of de tecting, locating, analyzing, isolating, and correcting suspected faults using the runtime information of a system. However, testing and debugging are not sufficient to prove the correctness of a safety-critical system. In contrast, static analysis is supported by formalisms to specify the system precisely. Formal verification methods are then applied to prove the logical correctness of the system with respect to the specification. Formal verifica tion gives us greater confidence that safety-critical systems meet the desired assurance properties in order to avoid disastrous consequences.
13th International Conference on Information Systems and Advanced Technologies “ICISAT 2023”
Author: Mohamed Ridda Laouar
language: en
Publisher: Springer Nature
Release Date: 2024-07-30
This book covers the Proceedings of the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023.” One of the evocative and valuable dimensions of this conference is the way it brings together researchers, scientists, academics, and engineers in the field from different countries and enables discussions and debate of relevant issues, challenges, opportunities, and research findings. The ICISAT’2023 conference provided a forum for research and developments in the field of information systems and advanced technologies and new trends in developing information systems organizational aspects of their development and intelligent aspects of the final product. The aim of the ICISAT’2023 is to report progress and development of methodologies, technologies, planning and implementation, tools, and standards in information systems, technologies, and sciences. ICISAT’2023 aims at addressing issues related to the intelligent information, data science, and decision support system, from multidisciplinary perspectives and to discuss the research, teaching, and professional practice in the field. The book of ICISAT’2023 includes selected papers from the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023,” organized during December 29–30, 2023. In this book, researchers, professional software, and systems engineers from around the world addressed intelligent information, data science, and decision support system for the conference. The ideas and practical solutions described in the book are the outcome of dedicated research by academics and practitioners aiming to advance theory and practice in this research domain. The list of topics is in all the areas of modern intelligent information systems and technologies such as neural networks, evolutionary computing, adaptive systems, pervasive system, ubiquitous system, E-learning and teaching, knowledge-based paradigms, learning paradigms, intelligent data analysis, intelligent decision making and support system, intelligent network security, web intelligence, deep learning, natural language processing, image processing, general machine learning, and unsupervised learning.