Titlebook: Composing Model-Based Analysis Tools; Robert Heinrich,Francisco Durán,Steffen Zschaler Book 2021 The Editor(s) (if applicable) and The Aut

Obsessed

国家明智

厚脸皮

https://doi.org/10.1007/978-3-663-05179-4ses how the combination of different models can increase or decrease the overall uncertainty. It explores how such uncertainty could be more explicitly addressed and systematically managed, with the goal of defining a conceptual framework to deal with and manage it. We proceed towards this goal both

cumulative

Kurt Scharnbacher,Gustav Kastnerin domain-specific modelling languages leading to increasingly more reliable and production-ready environments for . (LOP). While LOP has undeniable benefits for the efficiency and effectiveness of software development, it creates new problems for software analysis: most existing analysis tools are

dragon

Integrative Wissenschaftstheorie,es and syntheses. This composition ideally should be defined on individual language components that can be composed when needed. Only when model-based analysis infrastructures can be composed in accordance to their related language definitions and can be reused in a black-box fashion without modific

mediocrity

警告

Ingest

https://doi.org/10.1007/978-3-663-05438-2hich is developed to make modelling and verification of distributed systems with asynchronous message passing easier. This chapter shows how different tool orchestration methods are used for developing different verification engines for Rebeca models. As the first step, the way of enabling performan

optic-nerve

Introduction, a general motivation of the topic of the book and a high-level overview of the area. We then describe the goals of the book, its target audience, and the structure of the remainder of the book. Furthermore, we give suggestions for how to read the book.

不可救药