Generative Analysis Overview

2. SecretsofAnalysis © 2017 Clear View Training Limited About the author... • Dr Jim Arlow • Director of Clear View Training Limited • Consultancy and training in UML, BPMN, Meta Data Management, Requirements Engineering • Customers include British Airways, BBC, JP Morgan Chase, Aviva Investments, Vodafone • Senior Visiting Research Fellow at Bournemouth University • Author of UML, BPMN and other books (next slide) • Contact: • Linked in: http://uk.linkedin.com/in/jimarlow • www.clearviewtraining.com 2

3. SecretsofAnalysis © 2017 Clear View Training Limited Contents 3 • A high-level overview of some topics from our new book, “Secrets of Analysis” • Principles of Generative Analysis • M++ • If you want full details of the techniques, please buy our book: Available here: http://bit.ly/SecretsOfAnalysis

4. SecretsofAnalysis © 2017 Clear View Training Limited Our books... • Contact Clear View Training Limited (www.clearviewtraining.com) for BPMN and UML consultancy and training • We specialize in interactive texts 4 plus Interactive: Available in iBooks

6. SecretsofAnalysis © 2017 Clear View Training Limited What is Generative Analysis? • A new approach to OOA that tries to fill in the bits that other methods tend to leave out: • Understanding the process of communication • Dealing with unstructured, informal information (most of the information you get is like this). • Eliciting high quality information from information sources that are subject to: • Deletion • Distortion • Generalization • The goals of GA are to teach you how to deal with real-world human issues of software engineering and to provide you with suitable analysis tools 6

7. SecretsofAnalysis © 2017 Clear View Training Limited The beginnings of GA • GA begins with informal, unstructured information: • Partial - essential information is deleted • Inaccurate - essential information is distorted • Generalized - essential information is modified by rules and beliefs about the world which may or may not be true • To cope with this situation, GA provides you with: • Mapping tools - mind mapping, concept mapping, dialogue mapping • Specific ways to capture and organize unstructured, informal information • Four valued logic - a way to transform and enrich informal, unstructured information • M++ - a meta language for OOA • Specific techniques to recover deleted, distorted and generalized information • A set of transformations to generate high quality information from low quality information - this is the "generative" bit • Literate modeling - a way to increase the comprehensibility and value of UML models 7

8. SecretsofAnalysis © 2017 Clear View Training Limited Models are “maps” • Features of maps: • The map is not the territory • No map is a complete representation of its territory • Maps can be mapped (meta-models of the world) • Every map at least says something about the map maker • The most useful maps are abstract and precise 8 "Knowledge - the structural similarity between your nervous-system constructed maps and what you are mapping" [Kodish 1]

9. SecretsofAnalysis © 2017 Clear View Training Limited We are map makers... • Abstraction - “the selection of important details within a particular context” 9 Conceptual level: words Perceptual level: thinking feeling Sensory level: seeing hearing tasting touching Objective level: unspeakable unknowable What was that? Space-time event Abstraction Feedback

10. SecretsofAnalysis © 2017 Clear View Training Limited Three isomorphic models... • Which do you prefer? 10 deposit( amount : double ) withdraw( amount : double ) accountNumber : String owner : String balance : double BankAccount d( f : double ) e( g : double ) a : String b : String c : double X String alpha [] String beta [] double gamma [] function plus( i : int, delta : double ) function minus( i : int, delta : double )

11. SecretsofAnalysis © 2017 Clear View Training Limited Software sanity... • Convergent Architecture - • “Business engineering with object technology”, David A. Taylor (1995) • “Convergent Architecture”, Richard Hubert (2002), • “Secrets of Analysis”, Arlow and Neustadt (2013) 11 Sane Un-sane Black- box Does what you need it to do in a way that is intuitive and easy to understand. “Feels right” because it seems to be using the same map of the world as you are. Black box sanity is a necessary condition for software to interact successfully with human beings. Seems to be working according to an entirely different map to your own. Behaves in a way that is un-intuitive and (to you) illogical, un-sane and hard to understand. May or may not do what you need it to do. You may, or may not be able to get it to do what you need it to do, even if it can. White- box Has an internal structure that is clearly isomorphic to the structure of the problem domain. Because of this, its internal structure is understandable by domain experts. White-box sanity is not a necessary condition for software to interact successfully with human beings. Has an internal structure that may be isomorphic to the structure of the problem domain, but this isomorphism is very complicated. Is not understandable by domain experts. ✓

12. SecretsofAnalysis © 2017 Clear View Training Limited Establishing feedback • Every model is a theory about the world: • Test your theories against reality at the earliest possible opportunity! • Test by applying the principle of convergence (white-box sanity): • Present the model to domain experts and talk them through it. • Create a Literate Model. Does it read well? Can domain experts understand it? • Create a project glossary. Key abstractions in the problem domain must occur in your models and have the same meaning. • Execute your models • Never defend a broken model. Emotional investment is pointless – it is only a model! • Use software sanity and convergence as criteria for model assessment • Change or abandon a broken model as soon as you can! 12

14. SecretsofAnalysis © 2017 Clear View Training Limited The role of ontology 14 Common ontology Common mapping ontology Computer to computer Human to computer Human to human • Communication is predicated on common ontologies: • At a very fundamental level, we must be able to agree about the components of the world • Our maps must be isomorphic • Convergent Architecture • Software sanity

15. SecretsofAnalysis © 2017 Clear View Training Limited GA model of communication 15 presuppositions deletion distortion generalization external response external response internal response internal response Bob Alice presuppositions deletion distortion generalization presuppositions deletion distortion generalization presuppositions deletion distortion generalization

16. SecretsofAnalysis © 2017 Clear View Training Limited M++ drives GA • M++ is a pattern language for working with deletions, distortions and generalizations and recovering the elided information • M++ • Identifies - different types of DDG • De-abstracts - recovers DDG information • De-hypnotizes - tracks generalized and distorted information back to the real world (or not!) 16 Presuppositions Distortions: 1. Cause-effect 2. Complex equivalence 3. Mind reading 4. Nominalization 5. Propositional functions ++ Generalizations: 1. Lost performatives 2. Modal operators 3. Universal quantifiers 4. Ambiguous existential quantifier ++ 5. Ambiguous indefinite article ++ 6. Multidimensional terms ++ Deletions: 1. Simple deletion 2. Unspecified referential index 3. Comparative deletion 4. Unspecified verbs 5. Unspecified quantifier ++ 6. Unspecified temporal quantifier ++

17. SecretsofAnalysis © 2017 Clear View Training Limited Deletion • You pay attention to some things, while filtering out others. It is also known as negative hallucination. • Simple deletion - details have been deleted • Unspecified referential index - it is not clear to whom or what the statement refers. • Comparative deletion - a comparison is made, but the basis for the comparison is not stated. • Unspecified verbs - it is not clear how something is done. • Unspecified quantifier ++ - quantifiers such as many, more, most, few, etc. • Unspecified temporal quantifier ++ - the time interval at which something is true or during which something happens is unspecified. 17

18. SecretsofAnalysis © 2017 Clear View Training Limited Simple deletion 18 SIMPLE DELETION Definition: Simple deletion is where something is left out of the communication. Example: S: The contents of the library must be recorded in the new system. Detection pattern: Visualize the communication as an image or movie in your head (e.g. imagine the contents of the library). This is called representational tracking. Can the things you imagine be tracked back to the communication itself? Keywords: N/A Recovery pattern: Identify the missing information and ask for more details. Get the stakeholder to be specific. Recovery questions: What is/are...? Who is/are...? Can you be more specific about...? Can you list ...?

19. SecretsofAnalysis © 2017 Clear View Training Limited Distortion • You believe something to be true that is not true. • Cause-effect – you establish an explicit cause-effect relationship between two things that may or may not be true. • Complex equivalence - one thing, X, is taken to be synonymous with another thing, Y. • Mind reading - you claim to know what others think and feel. • Nominalization – you talk about a process or as though it is a thing. A verb or verb phrase is turned into a noun or noun phrase. • Propositional functions ++ – a statement about a multi- ordinal term that is neither true nor false until the value of the term is specified. May evaluate to true, false, maybe, or undefined. 19

20. SecretsofAnalysis © 2017 Clear View Training Limited Cause-effect 20 Definition: You establish an explicit cause-effect relationship between two things that may or may not be true. Cause-effect indicates a set of presuppositions about the world where X causes Y. There may be a true causal relationship between the two things, or cause-effect may merely be a presupposition. If it is a presupposition, it indicates the edge of your map. Cause-effect often removes the possibility of choice because one thing demands another. Example: S: I guess there'll be a lot of disruption while you are installing the new system. Detection pattern: Look for X causing Y. Keywords: if, then, because, makes, causes, implies, when, while, I guess, I suppose, so Recovery pattern: Challenge the cause-effect relationship. Recovery questions: How specifically does X cause Y? By what process does X cause Y? How do you know that X causes Y? Are there any circumstances in which X does not cause Y?

21. SecretsofAnalysis © 2017 Clear View Training Limited Generalization • Rules based on your cognitive map of reality. They arise from your presuppositions about the world, from the beliefs and values you create unconsciously from your life experiences. • Lost performatives – a judgment is made, but it is not clear who is making it. • Modal operators – modal operators of possibility constrain what is possible. Modal operators of necessity constrain what is necessary. • Universal quantifiers – are entirely inclusive or exclusive, and ignore fine distinctions. A statement using a universal quantifier is inherently fragile. • Ambiguous existential quantifier ++ – it is unclear whether "some" means "Some, but not all" ("some" used in a strictly logical sense) or "Some, or possibly all" ("some" used to cover uncertainty). • Ambiguous indefinite article ++ – the indefinite article "a" or "an" can sometimes act as a universal quantifier or as an existential quantifier. • Multidimensional terms ++ – see Chapter 13. 21

22. SecretsofAnalysis © 2017 Clear View Training Limited Universal quantifiers 22 Definition: Universal quantifiers are entirely inclusive or exclusive. They ignore fine distinctions and indicate the edges of someone’s map. A statement using a universal quantifier is inherently fragile, as it only requires one counter-example to disprove it. Example: S: I don't know why we bother issuing fines. No one ever pays them! Detection pattern: Look for the keywords below. Keywords: Inclusive: all, every, always, everyone, everything Exclusive: none, never, no one, nothing Recovery pattern: Use exaggeration. Ask for a counter example. Recovery questions: You mean absolutely every one in the whole world...? You mean no one has ever...? Can you think of a case when...?

23. SecretsofAnalysis © 2017 Clear View Training Limited Presuppositions drive DDG • Presuppositions - the rules and beliefs you have about the world • Whenever you receive a communication, you will go on an internal quest for meaning (transderivational search) to establish what it means. • The more abstract the communication, the more details you will have to supply for yourself and the more your presuppositions delete, distort and generalize the communication 23

25. SecretsofAnalysis © 2017 Clear View Training Limited Summary • We hope you have enjoyed this short introduction to some of our work • If you liked this presentation, you will love our book: 25 http://bit.ly/SecretsOfAnalysis