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Computer science. --- Domain specific languages.

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This textbook describes the theory and the pragmatics of using and engineering high-level software languages – also known as modeling or domain-specific languages (DSLs) – for creating quality software. This includes methods, design patterns, guidelines, and testing practices for defining the syntax and the semantics of languages. While remaining close to technology, the book covers multiple paradigms and solutions, avoiding a particular technological silo. It unifies the modeling, the object-oriented, and the functional-programming perspectives on DSLs. The book has 13 chapters. Chapters 1 and 2 introduce and motivate DSLs. Chapter 3 kicks off the DSL engineering lifecycle, describing how to systematically develop abstract syntax by analyzing a domain. Chapter 4 addresses the concrete syntax, including the systematic engineering of context-free grammars. Chapters 5 and 6 cover the static semantics – with basic constraints as a starting point and type systems for advanced DSLs. Chapters 7 (Transformation), 8 (Interpretation), and 9 (Generation) describe different paradigms for designing and implementing the dynamic semantics, while covering testing and other kinds of quality assurance. Chapter 10 is devoted to internal DSLs. Chapters 11 to 13 show the application of DSLs and engage with simpler alternatives to DSLs in a highly distinguished domain: software variability. These chapters introduce the underlying notions of software product lines and feature modeling. The book has been developed based on courses on model-driven software engineering (MDSE) and DSLs held by the authors. It aims at senior undergraduate and junior graduate students in computer science or software engineering. Since it includes examples and lessons from industrial and open-source projects, as well as from industrial research, practitioners will also find it a useful reference. The numerous examples include code in Scala 3, ATL, Alloy, C#, F#, Groovy, Java, JavaScript, Kotlin, OCL, Python, QVT, Ruby, and Xtend. The book contains as many as 277 exercises. The associated code repository facilitates learning and using the examples in a course.

Domain-specific programming languages. --- Application languages (Computer science) --- Domain-specific languages (Computer science) --- DSLs (Domain-specific programming languages) --- Little languages (Computer science) --- Macros (Domain-specific programming languages) --- Problem-oriented languages (Computer science) --- Programming languages (Electronic computers)

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Administrative law --- Droit administratif --- Eminent domain --- Expropriation --- Public use --- Utilité publique --- Cases --- Jurisprudence

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This book investigates how domain dependent quantities from geometry and physics behave when the domain is perturbed. Of particular interest are volume- and perimeter-preserving perturbations. The first and second derivatives with respect to the perturbation are exploited for domain functionals like eigenvalues, energies and geometrical quantities. They provide necessary conditions for optimal domains and are useful when global approaches like symmetrizations fail. The book is exampledriven and illustrates the usefulness of domain variations in various applications.

Nonlinear functional analysis --- Shape Optimization, Domain Variation, Pertubation, Spherical Harmonics, Eigenvalues.

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While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.

Distribution (Probability theory) --- R (Computer program language) --- Distribution (Probability theory). --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- GNU-S (Computer program language) --- Domain-specific programming languages