How to Understand Almost Anything

A Practitioner's (updated) Guide to Domain Analysis

You want to analyse, understand and capture the subject matter of a domain, typically because you want to build a software system in that domain? You're looking for abstractions to describe and ultimately execute said subject matter? Then this book is for you: it's chock-full of experience and practices for tackling this challenge.

Markus Voelter

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About

About the Book

Domain analysis is about understanding a body of knowledge, the subject matter of a domain. You want to understand the concepts, vocabulary, rules and constraints clearly enough so you can define a set of abstractions and their relationships, a formal model, a “language” that can be used to completely and unambiguously describe subject matter in the domain, and ultimately, to execute the subject matter. This book explains how to do this. After the introduction, the core of the book is three sections that cover the three aspects of domain analysis: collecting, thinking and validating. Chapter five zooms out beyond the core domain analysis activities and discusses related issues such as agility, business strategy and process change. The last chapter describes recurring ideas and concepts when defining abstractions. You can use those as reminders – or a checklist – of what to think about when designing your abstractions. The book is based on more than a decade of analysing domain and designing languages in a wide variety of domains from science to finance to public administration to healthcare.

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About the Author

Markus Voelter

Markus helps organisations uncover, understand and operationalize the knowledge at the core of their business, building a common foundation between business and IT. He designs and implements languages to capture and validate this knowledge, and to make it executable on modern IT platforms.

As a language engineer, he analyses domains; designs user-friendly languages and supporting analyses; implements language tools and IDEs; and architects efficient and reliable backends based on interpreters and generators. He also works on formalisms and meta-tools for language engineering.

For 20 years, Markus has consulted, coached and developed in a wide range of industries, including finance, automotive, health, science and IT. He has published numerous papers in peer-reviewed conferences and journals, has written several books on the subject and spoken at many industry conferences world-wide.

Markus holds a diploma in physical engineering from FH Ravensburg-Weingarten and a PhD in computer science from TU Delft.

Episode 260

An Interview with Markus Voelter

Table of Contents

Introduction

What is Domain Analysis • Domain versus System • A formal language • Programmable Platforms • Related Approaches • Domain Analysis and Dev Projects • People and Roles • Scale and Setup • The Domain Analysis Triangle • Terminology • What’s in the book • Who should read this

Collecting

Survey the Land • Written Material • Hidden Languages • The Right People • Organising Work • Consistent Terminology • Working Sessions • Active Listening • Consistency versus Change • Dealing with Uncertainty • Capture Results

Thinking

Time to Think! • Bounds of the Domain • Depth of the Solution • Removing Cruft • Abstraction • Test Support • Domain Crosscuts • Platforms • Industry Standards • Ups and Downs • Spread the Knowledge • Find a Critic

Validating

Domain Specification • Domain Implementation • Let Users Play • Implement Real Stuff • Conceptual Review • Analyse Usage • Dealing with Feedback • Great Demos • Writing

Supporting

Business & Strategy • Marketing & Communication • Agility & Roles • System & Process

Abstracting

Modularization • Encapsulation • Contracts • Contract Governance • Decoupling • Viewpoints • Annotations • Layers • Parametrisation • Standard Library • Types and Instances • Hierarchical Decomposition • Specialization • Open versus Closed World • Constraints • Weak Structure • Declaration over Implementation • Bottom-up Abstraction • Derived Properties • Taxonomies • Prescription versus Inference • Intra-Model Crosscheck • Execution Paradigms • Naming, Identity and References • Composition versus Reference • Versioning • About Nothing • On Errors |(Don’t) Go Meta

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