Formal Verification for VRM Requirement Models
Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 878 LNEE, Page: 961-969
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
At the requirements level, formal verification and analysis are the focus of task’s attention which is developing complex systems by formal methods. Model checking is a technique for analysis and automated verification of complex safety-critical software systems. In this paper, a requirement model verification method based on formal technology is proposed to practice the model checking activity into the development process. Firstly, this essay analyzes syntax and semantics of models, which are defined by tabular expressions in VRM (variables relationship model). Then we preprocess the VRM model to classify into events tables, conditions tables and model class tables, and transform the VRM model into the automaton state transfer diagram with the help of semantic complementary work. Finally, we design an automatic model transformation framework from the VRM model to the model verification tool (nuXmv) and implement a translator between the formal specification language VRM and the symbolic model checker nuXmv. In this paper, we discuss our translation and abstraction approach in some depth and illustrate its feasibility with some preliminary examples.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128736763&origin=inward; http://dx.doi.org/10.1007/978-981-19-0390-8_121; https://link.springer.com/10.1007/978-981-19-0390-8_121; https://dx.doi.org/10.1007/978-981-19-0390-8_121; https://link.springer.com/chapter/10.1007/978-981-19-0390-8_121
Springer Science and Business Media LLC
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