Objects with Attitudes

Identities and Aspects

Despite its object and unified vocations, the OMG’s UML (Unified Modeling Language) has been sitting uneasily between scopes (e.g requirements, analysis, and design), as well as between concepts (e.g objects, aspects, and domains).

Where to look  for AAA issues (Maurizio Cattelan)

Those misgivings probably go a long way to explain the limited, fragmented, and shallow footprint of UML despite its clear merits. Hence the benefits to be expected from a comprehensive and consistent approach of object-oriented modeling based upon two classic distinctions:

  • Business vs System: assuming that systems are designed to manage symbolic representations of business objects and processes, models should keep the distinction between business and system objects descriptions.
  • Identity vs behavior: while business objects and their system counterpart must be identified uniformly, that’s not the case for aspects of symbolic representations which can be specified independently.

That two-pronged approach bridges the gap between analysis and design models, bringing about a unified perspective for concepts (objects and aspects) as well as scope (business objects and system counterparts).

Object Oriented Modeling

Object Oriented and Relational approaches are arguably the two main advances of software engineering for the last 50 years. Yet, while the latter is supported by a fully defined theoretical model, the former still mostly stands on the programming languages supporting it. That is somewhat disappointing considering the aims of the Object Management Group (OMG),

UML was born out of the merge of three modeling methods: the Booch method, the Object-modeling technique (OMT) and Object-oriented software engineering (OOSE), all strongly marked by object orientation. Yet, from inception, the semantics of objects were not clearly defined, when not explicitly confused under the label “Object Oriented Analysis and Design” (OOA/D). In other words, the mapping of business contexts to system objects, a critical modeling step if there is any, has been swept under the carpet.

Literal Bird

That’s a lose/lose situation. Downstream, OO approaches, while widely accepted at design level, remain fragmented due to the absence of a consensus regarding object semantics outside programming languages. Upstream, requirements are left estranged from engineering processes, either forcing analysts to a leap of faith over an uncharted no man’s land, or to let business objects being chewed up by programming constructs.

Domains and Images

In mathematics, an image is the outcome of a function mapping its source domain to its target co-domain. Applied to object-oriented modeling, the problem is to translate business objects to their counterpart as system components. For that purpose one needs to:

  1. Define domains as sets of business objects and activities whose semantics and life-cycle are under the authority of a single organizational unit.
  2. Identify the objects and phenomena whose representation has to be managed, as well as the lifespan of those representations.
  3. Define the features (attributes or operations) to be associated to system objects.
  4. Define the software artifacts to be used to manage the representations and implement the features.
From Business Domain to System Image

While some of those objectives can be set on familiar grounds, the four must be reset into a new perspective.

Business Objects are rooted in Concerns

Physical or symbolic, objects and activities are set by concerns. Some may be local to enterprises, some defined by common business activities, and some set along a broader social perspective. The first step is therefore to identify the organizational units responsible for domains, objects identities and semantics:

  • Domains in charge of identities will govern objects life-cycle (create and delete operations) and identification mechanisms (fetch operations). That would target objects, agents, events and processes identified independently of systems.
  • Domains in charge of semantics will define objects features (read and update operations). That would target aspects and activities rooted (aka identified) through primary objects or processes.
Context anchors and associated roles and activities

It must be noted that whereas the former are defined as concrete sets of identified instances governed by unique domains, the latter may be defined independently of the objects supporting them, and therefore may be governed by overlapping concerns set by different domains.

Objects and Architectures

Not by chance, the distinction between identities and features has an architectural equivalent. Just like buildings, systems are made of supporting structures and subordinate constructs, the former intrinsic and permanent, the latter contingent and temporary. Common sense should therefore dictate a clear distinction between modeling levels, and put the focus on architectures:

  • Enterprise architecture deals with objectives, assets and organization associated with the continuity of corporate identity and business capabilities within a given regulatory and market environment. That is where domains, objects and activities are identified and defined.
  • Functional architecture deals with the continuity of systems functionalities as they support the continuity of business memory and operations. At this level the focus is not on business objects or activities but on functions supported by the system: communication, control, persistency, and processing.
  • Technical architecture deals with the feasibility, efficiency and economics of systems operations. That is where the software artifacts supporting the functions are designed .
Objects and Architectures

Objects provide the hinges binding architectural layers, and models should therefore ensure direct and transparent mapping between business objects, functional entities, and system components. That’s not the case for features whose specification and implementation can and should be managed separately.

Fleshing out Objects with Semantic Aspects

Confusing business contexts with their system counterparts leads to mistaken equivalence between features respectively supported by business objects and system artifacts:

  • The state of physical objects may be captured or modified through specific interfaces and persistently recorded by symbolic representations, possibly with associated operations.
  • Non physical (notional) business objects are identified and persistently recorded as such. Their state may also appear as transient objects associated with execution states and processing rules.
  • Events have no life-cycle and therefore don’t have to be identified on their own. Their value is obtained through interfaces; associated messages can be used by control or processing functions; values can be recorded persistently. Since the value of past events is not meant to be modified operations are irrelevant except for interfaces.
  • Actual processes are identified by execution context and timing. There state may be queried through interfaces and recorded, but persistent records cannot be directly modified.
  • Symbolic processes are identified by footprint independently of actual execution. Their execution may be called through interfaces and the results may be recorded, but persistent records cannot be directly modified.
  • External roles are identified by the interfaces supporting the interactions. Their activity may be recorded, but persistent records cannot be directly modified.
Fleshing out Aspects

By introducing complementary levels of indirection between business and system objects on one hand, identities and features on the other hand, the proposed approach significantly further object-oriented modeling from requirements analysis to system design. Moreover, this approach provides a robust and effective basis for the federation of business domains, by modeling separately identities and semantic features while bridging across conceptual, logical and physical information models.

Untangling Business Rules

However tangled and poorly formulated, rules provide the substance of requirements as they express the primary constraints, needs and purposes. That jumble can usually be reshaped differently depending on perspective (business or functional requirements),  timing constraints (synchronous or asynchronous) or architectural contexts; as a corollary, the way rules are expressed will have a significant impact on the functional architecture of the system under consideration. Hence, if transparency and traceability of functional and technical arbitrages are to be supported, the configuration of rules has to be rationalized from requirements inception. And that can be achieved if rules can be organized depending on their footprint: domains,  instances, or attitudes.

From Objects to Artifacts

Requirements analysis is about functional architecture and business semantics, design is about software artifacts used to build system components. The former starts with concrete descriptions and winds up with abstract ones, the latter takes over the abstractions and devise their concrete implementation.

Uphill to functionalities, downhill to implementations

Somewhat counter-intuitively, information processing is very concrete as it is governed by actual concerns set from biased standpoints. Hence, trying to abstract requirements of supporting systems up to some conceptual level is a one way ticket to misunderstandings because information flows are rooted in the “Here and Now” of business concerns. Abstract (aka conceptual) descriptions are the outcome of requirements analysis, introduced when system symbolic representations are consolidated across business domains and processes.

Starting with a concrete description of identified objects and processes, partitions are used to analyze the variants and select those bound to identities. Inheritance hierarchies can organized accordingly, for objects or aspects.

Inheritance of identities vs inheritance of aspects.

While based on well understood concepts, the distinction between identity and aspect inheritance provides a principled object-oriented bridge between requirements and models free of any assumption regarding programming language semantics for abstract classes or inheritance.

Objects, attitudes, and Programming Languages

Because object-oriented approaches often stem from programming languages, their use for analysis and design is hampered by some lack of consensus and a few irrelevant concepts. That is best illustrated by two constructs, abstract classes and interfaces.

  • Most programming languages define abstract classes as partial descriptions and, as a result, the impossibility to be instantiated. When applied to business objects the argument is turned around, with the consequence, no instance, taken as the definition.
  • Interfaces are also a common features of object-oriented languages, but not only, as they may be used to describe the behavior of any software component.

Those distinctions can be settled when set within a broader understanding of objects and aspects, the former associated with identified instances with bound structures, eventually implemented as concrete classes, the latter with functionalities, eventually implemented as abstract classes or interfaces.

From Analysis to Design

A pivotal benefit of distinguishing between objects identity and aspects is to open a bridge between analysis and design by unifying respective patterns along object-oriented perspective. Taking a cue from the Gang of Four, system functionalities could be organized along three basic pattern categories:

  • Creational functionalities deal with the life-cycle (create and delete operations) and identification mechanisms (fetch operations)  of business objects whose integrity and consistency has to be persistently maintained independently of activities using them.
  • Structural functionalities deal with the structure and semantics of transient objects whose integrity and consistency has to be maintained while in use by activities. They will govern features (read and update operations) and target aspects and activities rooted (aka identified) through primary objects or processes.
  • Behavioral functionalities deal with the ways objects are processed.

Mapping analysis patterns to design ones will greatly enhance models traceability; moreover, taking advantage of the relative maturity of design patterns, it may also boost quality across model layers as well as the whole effectiveness of model driven engineering solutions.

Objects Oriented Modeling and Model Driven Engineering

The double distinction between contexts and systems on one hand, objects and aspects on the other hand, should help to clarify the contents of modeling layers as defined by OMG’s model driven architecture (MDA):

  • Computation independent models (CIMs) are structured around business objects and processes identified on their own, associated with organizational details for roles and activities.
  • Platform independent models (PIMs) are organized on two levels, one for functional architectures (boundaries, processes, persistency, services, communication), the other one for associated aspects.
  • Platform specific models (PSMs) are similarly designed on two levels, one mapping functional architectures, the other one implementing aspects.
MDA with UML#

Using  UML#, object-oriented concepts can therefore be applied uniformly from requirements to design without forcing programming semantics into models.

Further Readings

2012: Ahead with the New Year

New Grounds or New Holes ?

New years bring new perspectives, but looking ahead is useless without a sound footing. These plain figures may shed some light on the matter.

vvvv
Looking ahead with hindsight (Where to look  for AAA issues (Maurizio Cattelan)

What: Requirements and Models

Projects should start with some agreement about expectations and commitments. Maturity on that regard can be estimated with:

  • Number of projects started on agreed (actual meeting between stakeholders and providers) requirements, relative to all started developments.
  • Number of agreed requirements as sanctioned by models, relative to all requirements.
  • Number of agreed requirements that included quality plans, relative to all agreed requirements.
  • Number of root artifacts linked to requirements items relative to all root artifacts.
  • Number of requirements items linked to root artifacts relative to all requirements items.

The critical point here is the traceability between rough requirements as initially expressed, and structured and non ambiguous ones agreed upon after analysis.

Who: Stakeholders, Users, Developers

If their maturity is tobe assessed and improved, engineering projects should clearly distinguish between roles, even when they are played by the same persons or in tight collaboration. Here some clues to find out what happens:

  • Planned meetings with differentiated positions relative to all planned meetings.
  • Decision making meetings relative to all planned meetings.
  • Non functional agreed requirements relative to all agreed requirements.
  • Changes in agreed requirements linked to decision makers relative to all changes in agreed requirements.

The focus here should be on the definition of domains and use cases on one hand, traceability on the other hand.

When: Planning

As almost every human endeavour, projects’ success is governed by time and resources, in that case the delivery of system functionalities on time and on budget. On that regard, process maturity assessment should start with:

  • Number of projects not deployed relative to projects started on agreed requirements
  • Time spent in decision-making meetings relative to total project time.
  • Actual resources relative to estimations after agreed requirements.
  • Elapsed time between applications ready to be deployed and actually operational relative to projects duration.

The critical factors here are the traceability of model contents and the mapping of development flows into work units.

How: Tools

Engineering processes are meant to be supported by tools but that’s not necessarily for the best. A rough diagnostic can be based upon:

  • Number of tools installed relative to the number of functions supported by those tools.
  • Number of tools installed during the last year relative to the number of  tools installed.
  • Number of exchanges operated between tools relative to the number of  tools installed.

Further assessment should be set within the MDA/MDE perspective according model transformation policies.

A Time For Every Purpose

“The only reason for time is so that everything doesn’t happen at once.”

Albert Einstein.

Time and Events

Time is what happens between events. Without events there will be no time, and each event introduces its own time-scale.

Time is what persists of events (S.Dali)

From the (limited) perspective of system requirements, time is the scale used to position business processes, i.e the execution of business activities. And since systems are meant to support business processes, time must be set by business events before being measured by shared devices.

As a counter intuitive corollary, time is only to run with business: if nothing relevant is meant to happen between two events there is no need of time and both events can be coalesced into a single one. Conversely, if nothing is allowed to happen between two events the system will have to dealt with them in real-time.

Time Measurement

Like events, time is necessarily local since physical devices must sit on location, possibly mobile ones; once set by some triggering event, time can only be measured by a single clock and therefore cannot be shared across distributed systems.

Time is local but one can take it along

Moreover, along with its measuring device, time is also an artifact built to meet specific concerns. That can be very useful if activities are to be managed in isolation. Like different objects managed within different address spaces, different activities  could be time-stamped and controlled by different time scales.

Time scales can be designed on purpose

Being set by independent clocks, each built to answer it own concerns, the symbolic representations of activities could then be fenced off  so their execution will appear as impervious to external events. Yet, if and when architectural constraints are to be properly identified, execution units will have to be timed along some shared time scale. More generally, distributed activities, being executed along different time scales, would have to be synchronized through a logical clock using its own time scale. Another solution is to synchronize physical clocks using a standard protocol like the  Network Time Protocol (NTP).

Distributed activities have no time of their own

Along that reasoning, real-time activities can only be supported locally: if events must occur simultaneously within and without the system, i.e nothing is supposed to nothing is supposed to happens between changes in actual objects and their symbolic representation, that can only be achieved under the control of a single system.

Requirements Rounds up

Principles

Whereas it is based upon well known concepts and accepted standards, the Caminao approach entails a new modeling perspective which calls for change of habits, mostly at requirements level. The objective here is to experiment some Proof of  Concept by contriving requirements on-line along the Caminao path.

Jeff_Wall-noticias
Collecting Requirements (Jeff Wall)

For that purpose the proposed experiment makes use of four principles:

  • Crowd-sourcing: except for Caminao stereotypes, understanding do not come from a special expertise or best-practices but is built on collective wisdom.
  • Iterations: stakeholders and analysts are circling topics until they agree on clear and unambiguous pictures.
  • Illustrations: requirements begin as expectations, as such they should be best captured through pictures before being analyzed through models.
  • Assertions: requirements are meant to translate into commitments, hence, associated models should be settled by explicit constraints and expressions.
Requirements loops: from expectations to commitments.

On that basis stakeholders will introduce their requirements as illustrations, analysts will try to translate them into models which, after being accepted by stakeholders,  will subsequently be decorated by assertions.

Modus Operandi

  • Requirements rings are managed through the  G+ Caminao Rings page.
  • In order to submit a project, candidate stakeholders must belong to the circle of fellows.
  • Fellows stakeholders may submit projects by creating their own G+ pages and circles and identifying them with the Caminao G+.  A new page is created for each project, to be matched with the fellow G+ circle.
  • Fellow analysts propose models capturing all or parts of illustrated requirements.
  • Stakeholders may accept, reject, or hold back their decision. Refusals can be commented but reservations can only be qualified with additional illustrations.
  • Once approved models may subsequently be fleshed out with expressions, constraints and rules.

Models

Fellow analysts can propose two types of models:

  • Horizontal models describe individual artifacts and their connections.
  • Vertical models are anchored to single artifacts and focus on their partitions and inheritance relationships.

While it’s recommended to walk along basic UML conventions, models may include any kind of artifacts providing they are qualified by Caminao stereotypes for actual or symbolic objects and activities, roles, or events.

UMLSharp_Stereos
Caminao stereotypes for nodes

Stereotypes for containers use the same principle for organizational units (110) physical locations (121), physical executions (141), business domains (120), business activities (140).

The semantics of connectors (association, flow, transition, or channel) can remain implicit and defined by context. They may be stereotyped using standard set operators.

Set-based stereotypes for Connectors

By convention, objects, events and roles are labelled with singular nouns, activities use infinitive verbs, and processes use present progressive ones. Containers are named with plurals.

Who’s in the Loops

Four types of players may appear in requirements loops:

  • Stakeholders (one by project) set the context and objectives with pictures, photos or drawings. Textual descriptions are not allowed. Stakeholders accept or reject artifacts.
  • Users and business analysts add to the stakeholder requirements using the same media (no texts); they also may qualify model artifacts with formal expressions, constraints or rules.
  • System analysts suggest artifacts.
  • Architects (one by project) accept or reject qualifications on artifacts.
Who’s in the Loop

Mind Your Words

Language and meanings may be baffling bedfellows, as what is said is not necessarily what is meant. As a boost to requirements transparency, a simple gizmo may be used  by players to speak their mind, for their interlocutor (talking bubble) or only for the audience (thinking bubble).

Say What You Mean, Mean What You Say

Price Your Words

Assuming clear understanding and good faith, customers and providers must agree on a price, and for that purpose they must align their respective expectations and commitments.

Expecting to take advantage of business opportunities at a given time, customers define system requirement along a black box perspective; in return, providers analyze those requirements along a white box perspective and make an estimate of cost and duration. Their respective expectations are consolidated and commitments made, customers regarding payment, provider regarding delivery.

As far as customers are concerned, success is measured by the return on investment (ROI), which depends on cost, quality, and timely delivery. Providers for their part will design the solution, develop the components, and integrate them into targeted environments. Narrowly defined, their success will be measured by costs. Those concerns may be played along a non-zero sum game:

  • Customers assess the benefits (a) to be expected from the functionalities under consideration (b).
  • Providers consider the solutions (a) and estimate their costs (b).
  • Customers and providers agree on functionalities, costs and schedules (c).
Matching respective expectations and commitments of customers and providers.

Hence, while stakes are clearly conflicting on costs, there is room for collaboration on quality and timing, and that will bring benefits to both customers and providers.

Square the Rings

Even for standalone applications, it’s safe to assume that requirements will have to take into account external factors and constraints. Since those requirements will usually be managed by different organizational units, they must be sorted out upfront:

  • Non functional constraints deal with performances and resources.
  • Cross functional requirements deal with system functionalities shared by different business processes.
  • Application specific requirements deal with system functionalities supporting a single business process.
Squaring requirements rings

Those rings are used to organize projects according the requirements architectural footprint and associated responsibilities.

Caminao Charter

Objective

Caminao aims to chart all worlds of system models, drawing maps with a kernel of the UML, based upon a reasoned conceptual framework.

Set from a comprehensive and objective survey of system functionalities and architectures, using standard notation applied to unambiguous concepts, those maps should provide all-weather guidance to system modellers, whatever their bearings or creed.

Models, Architectures, Perspectives (MAPs)

Of all industrial artifacts, software components are the only ones that can be fully built from models. As a consequence, charting comprehensive and reliable maps should not only be feasible but also highly beneficial.

Caminao maps are built from models, architectures, and perspectives:

  • Models set the stages, where targeted artifacts are defined depending on concerns.
  • Topography put objects into perspective as set by stakeholders situation: business objectives, system functionalities, system implementation.
  • Concerns and perspectives must be put into context as defined by enterprise, functional or technical architectures.

The aim of those maps is to provide reasoned tools for seasoned modellers, helping them in setting milestones, planning journeys, and appraising itineraries.

  • Milestones are about sequences: they are necessary whenever expectations and commitments are set across different organizational units.
  • Planning is about projects: once requirements are properly analyzed, maps can be used to sequence goals, set paths, and define tasks gauged according topography metrics.
  • Appraising is about processes: given sound and objective metrics, tasks traceability to outcomes, and projects built alongside, roadmaps can be turned into development patterns depending on capabilities assessment.

Language

Maps are to be drawn using a standard notation, and for that purpose Caminao uses a kernel of OMG’s Unified Modeling Language.

UML# (for “charpente”, supporting structure in French),  is built on a core of UML syntactic constructs, using its stereotyping mechanism to define semantic qualifiers set along functional layers on one hand, OMG’s Model Driven Architecture on the other hand.

UML# objective is therefore not to be a substitute to UML but rather a complement dedicated to requirements and analysis, without affecting continuity with design and implementation models.

Concepts

Neither maps nor languages are meant to convey any guidance about course or discourse. Hence, modeling languages have nothing to say about what is to be represented and how it should be done. For that purpose a reasoned understanding of system functionalities and architecture is required.

Curiously, while core concepts are already at hand, most of methodologies are either aimed at system design, or lean on fallacies about what analysis models represent.

Caminao ultimate objective is therefore to bridge the conceptual gap between functional requirements and system analysis, bringing both semantics under a common roof. To that end, some principles are to be carried through:

  • Comprehensive scope: concepts must deal with all and every configuration, without assuming any restriction about functional requirements.
  • Closed set of concepts: all descriptions of system functionalities and architecture must be upheld by a limited and finite number of concepts.
  • Thorough and reasoned understanding: all stereotypes are to be unambiguously defined using formal expressions built from core concepts.
  • No expertise or best practices: maps must support all ventures and befit every methodological inclinations. As a consequence concepts and stereotypes must remain neutral and free of any preference or precedence.

Eventually, that should open a new perspective to model driven engineering by consolidating model layers around functional architectures.

UML# Manifesto

Objective

Taking a cue from Ivar Jacobson (“The road ahead for UML“), some modularity should be introduced in order to facilitate the use of UML in different contexts, organizational, methodological, or operational.

Avery_Singer2
“Charpente” is french for supporting structure (Avery Singer)

 

Three main overlapping objectives should be taken into consideration:

  • Complexity levels: the language features should be regrouped into clearly defined subsets corresponding to levels of description. That would open the way to leaner and fitter models.
  • Model layers: the language constructs should be re-organized along MDA layers if models are to map stakeholder concerns regarding business requirements, system functionalities, and system design.
  • Specificity: principled guidelines are needed for stereotypes and profiles in order to keep the distinction between specific contents and “unified” ones, the former set with limited scope and visibility, the latter meant to be used across model layers and/or organizational units.

As it happens, a subset of constructs centered on functional architecture may well meet those three objectives as  it will separate supporting structures (“charpentes” in french) from features whose specifications have no consequences on system architectures.

State of The Art

Information technologies progress crab-like, the hardware leg striding forward with Moore Law, and the software leg crawling on practices, with rare real leaps like relational (more than 30 years ago) and object (10 years later) technologies. The adoption of UML as a modeling standard at the end of the last century could have spurred a new start of innovation for software engineering; instead, its protracted diffusion and shallow or biased usage suggest a latent factor behind the limping progresses of software technologies. The absence of clear advances for the last 20 years, despite the undisputed soundness and cogency of available concepts and tools, may hint at some basic focusing error regarding what is to be considered.

Whereas object oriented approaches to software analysis and design are now broadly accepted, there is no consensus regarding the nature of targeted objects; more precisely, there is no explicit distinction between symbolic objects and processes on one hand, and their operational counterparts on the other hand. Such a confusion can be observed at different levels:

  • Requirements: the perspective is limited to a dual distinction between problem (what) and solution (how) spaces. That approach is much too simplistic as it overlooks the functional dimension, namely how a software solution (nothing more than a piece of code) is to be used within operational systems (a distributed set of agents, devices, and symbolic machines).
  • Time-scales: when models are used (that’s not always the case), operational contexts (business objects and processes) are captured at inception time and their description frozen as snapshots until further notice. This lack of synchronized context and system models rules out any explicit management of systems life-cycles and transformation.
  • Model contents: the aim of model driven (or based) approaches is to define development flows in terms of models and organize engineering processes accordingly. But that may not be possible if the semantics of model contents are not properly differentiated between modeling stages, the consequence being a lack of principled support for model transformation. That point is best illustrated by the perplexing debate about executable models and models as code.
  • Architectures: blurred focus on systems and contexts necessarily entails a confusion between enterprise, functional, and technical architectures. That confusion is proving to be critical when service oriented architectures (SOA) are considered.
  • Processes: the aim of development processes is to manage concerns set by different stakeholders, based upon different rationales, and subject to changes along different time-frames. If those concerns cannot be specified independently, the design and assessment of engineering processes will lack a sound basis.

Yet, since all those problems stem from the same blurred focus, they may also be deal with a shift in modeling paradigm. Moreover, that shift could be especially productive given the availability of the conceptual constructs associated with object oriented approaches and UML. For that purpose it is necessary to clarify model purposes and customize the language accordingly.

Two Legs and a Bridge

From a general perspective, and beyond lexical controversies, models and architectures should be defined along two parallel scales:

  • Architectures: enterprise (concepts), systems (functionalities), and platforms (technologies).
  • Models: conceptual (business context and organization), analysis (symbolic representations), design (physical implementation).

Caminao is focused on models as bridges between business goals and system implementation. While simple or standalone applications may often be developed without mediation, that’s not the case for shared applications deployed across distributed system with independent life-cycles.

  • Enterprise architecture: business requirements are not necessarily expressed with modelling languages.
  • Functional architecture: functional (aka system) requirements should be expressed with modelling languages.
  • Technical architecture: non functional requirements are not expressed with modelling languages.
Dial M for Models

Grammars and Semantics

Whatever the editor, graphical or otherwise, models are built from expressions according to grammatical rules. Some rules are meaningless as they only define what is possible, i.e how to form correct expressions; others are mixed as the associated constructs may also convey some meaning.

Expressions are not necessarily textual: this one means “Sign Language”

In any case, those rules have to deal with three types of considerations: lexical, syntactic, and semantics.

  1. Legitimate items, words or symbols, are defined by the lexical layer.
  2. The way lexical items can be used to build well-formed expressions is defined by syntactic rules.
  3. Finally, language semantics define the relationships between expressions and targeted contexts.

The Unified Modeling Language (UML) is the outcome of the collaboration between James Rumbaugh with his Object-modelling technique (OMT), Grady Booch, with his eponymous method, and Ivar Jacobson, creator of the object-oriented software engineering (OOSE) method. Whereas the Three Amigos did a pretty good job in consolidating the best of different approaches, UML 1.x was not built from scratch along the ideal template: instead of drawing clear lines between lexical, syntactic, and semantics layers, constructs were first established along semantic perspectives, namely static (aka structural) and dynamic (aka behavioral) views. Formal syntactic definitions of qualifiers and expressions came almost as an afterthought.

As is often the case when ambitious objectives set by visionaries are taken over by committees, things certainly didn’t improve with UML 2.x. Having to face a growing complexity without a solid grammatical ground, the OMG turned simultaneously to abstraction and specificity: on one hand Meta-Object Facility (MOF) is meant to bypass classical grammars descriptions by bringing every modeling language under a single meta-language roof; on the other hand stereotypes and profiles provide a very practical way to tailor UML to specific domains and organizations.

But those options have driven UML into opposing directions: on one hand meta-models do nothing to improve UML capabilities and usability as their aim is mainly to enable some interoperability between tools providers; on the other hand stereotypes and profiles push UML users into specific corners, which is not what a “unified” modelling language is supposed to do. As a consequence, more than 15 years since it became a standard, UML is still not widely accepted as such; and when it is used, it is all too often on a very limited scope, or for very specific purpose, or without much of methodology.

Lean, fit, and Sharp, as in “Charpente”

The proposed approach takes advantage of UML stereotyping mechanism to reorganize the semantics around a kernel of simple syntactic constructs dealing with:

Syntactic Constructs for Charpente Descriptions

All structures (nodes, connectors and features) are to be described uniformly with a single set of standard operators.

The semantics layer is to use a finite set of stereotypes to characterize:

  • Artifacts life-cycle, transient or persistent.
  • Artifacts nature, actual or symbolic.
  • Identification mechanisms: standalone, dependent, joint, continuous.
Charpente semantics

Traceability with “Charpentes”

Finally, semantics are needed to support built-in traceability of dependencies across models or along development cycles.

Built-in Traceability

Those dependencies must be managed depending on their scope and nature.

Regarding scope, one must distinguish between dependencies involving operational contexts and those limited to symbolic artifacts.

Modeling dependencies stem from requirements and refer to both operational contexts and their symbolic representations:

  • Representation dependencies between business objects or behaviors and their symbolic counterparts.
  • Structural dependencies between roots and members.
  • Functional dependencies between connected nodes.

Development dependencies arise from modeling decisions and refer solely to symbolic artifacts:

  • Ownership dependencies between address spaces and artifacts.
  • Usage dependencies through reused features, either shared or inherited.
  • Abstraction dependencies

Regarding the nature of dependencies, a clear-cut distinction must be maintained between those rooted to decisions and those born from necessity.

By-products

Taking inspiration from the Capability Maturity Model Integration (CMMI), the benefits of UML# and, more broadly, from architecture driven modelling can be identified for product, project, and process areas:

  • Traceability is obviously a starting point as it is a prerequisite for streamlined engineering (product), portfolio and risk management (project), and application life-cycle management (process).
  • Measurement comes close, with built-in unbiased estimators, project workloads, and process assessment.
  • Quality management would clearly benefits from layered traceability and objective measurements, with built-in controls, non-regressive testing, and model-based validation.
  • Reuse provides another path to quality, with patterns (product), profiles (project) and development strategies (processes).
  • Finally, collaboration is to be facilitated between engineering processes targeting heterogeneous platforms, using different methodologies, across independent organizations.

That should open new perspectives to projects productivity and effectiveness.

Implementation

A shallow implementation will simply add  UML# stereotypes to core UML diagrams.

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Shallow implementation: UML# stereotypes can be applied to nodes, references, connectors, and operators for composition and inheritance

Depending on tools it may be possible (e.g with No magic) to customize diagrams or even define new ones.

A deep implementation will entail the development of a two-level interpreter, one for UML# syntax, the other for the semantics.

cccc
Deep implementation: models semantics are added to a core of syntactic constructs

 

 

The Book of Fallacies

Objectives

Whereas the design side of software engineering has made significant advances since the introduction of Object Oriented approaches, thanks mainly to the Gang of Four and others proponents of design patterns, it’s difficult to see much progress on the other (and opening) side of the engineering process, namely requirements and analysis. As such imbalance creates a bottleneck that significantly hampers the potential benefits for the whole of engineering processes, our understanding of requirements should be reassessed in order to align external and internal systems descriptions;  in other words, to put under a single modeling roof business objects and processes on one hand, their system symbolic counterparts on the other hand.

Magritte’s Fallacy

Given that disproving convictions is typically easier than establishing alternative ones, it may be necessary to deal first with some fallacies that all too often clog the path to a sound assessment of system requirements. While some are no more than misunderstandings caused by ambiguous terms, others are deeply rooted in misconceptions sometimes entrenched behind walled dogmas.

Hence, to begin with a tabula rasa, some kind of negative theology is required.

#1: Facts are not what they used to be

Facts are not given but observed, which necessarily entails some observer, set on task if not with vested interests, and some apparatus, natural or made on purpose.  And if they are to be recorded, even “pure” facts observed through the naked eyes of innocent children will have to be translated into some symbolic representation. Nowadays that point is taking center stage due to the onslaught of big data (see Data Mining & Requirements Analysis).

#2: Truth and Objectivity are not to be found in Models

The mother of all fallacies is to think that models can describe some real-world truth. Even after Karl Popper has put such a fallacy to rest in sciences more than half a century ago, quite a few persist, looking for science in requirements or putting their hope in abstraction. Those stances cannot hold water because models necessarily reflect business and organizational concerns, expressed at a given time, and set within specific contexts. Negative theology provides an antidote: if a model cannot be proven as true, at least it should be possible to check that it cannot be falsified, i.e is consistent with contexts and concerns.

#3: Requirements are not meant to be “Engineered”

Taking for granted that all requirements can be directly “engineered” is to overlook the role of architectures between stakeholders and users expectations on one side, systems capabilities on the other side. Such assumption is to blur the respective responsibilities of business and system analysts, and induces the latter to second-guess the former. What may be a practical shortcut for standalone applications becomes a major risk factor when robust and stable architecture capabilities are to support constant adaptations to changing business needs.

#4: Objects can be found everywhere

Object Oriented approaches are meant to deal with the design of software components, not with business objects and organization. While it may be useful to look at business contexts from an OO perspective, there is no reason to assume that business objects and processes can be analyzed using the semantics of software design: hope is no substitute for methodology.

#5: “Natural” languages can be applied to every domain

Except for plain applications (calling for trivial solutions), significant business domains rely on specific and often very formal languages that will have to be used to express requirements. That may be illustrated with examples from avionics to finance, not to mention law. When necessary, modeling languages are to provide a bridge between specific (domains) of and generic (software) descriptions.

#6: Business concerns are “Conceptual”

Whatever the meaning of the adjective “conceptual”, it doesn’t fit to business concerns. Hence, trying to bring requirements to some conceptual level is a one way ticket to misunderstandings. Business concerns are very concrete indeed, rooted in the “here” and “now”  of competitive environments, and so must be the requirements of supporting systems. Abstract (aka conceptual) descriptions will be introduced at a later stage in order to define the symbolic representations and consolidate them as software components.

#7: Model is Code

If models were substitutes for code, or vice versa, that would make software engineering (and engineers) redundant. Surprisingly, the illusion that the information contained in models is the same as the one contained in programs (and vice versa) has sometimes wrongly taken from the Model Driven Engineering paradigm, despite a rationale going the opposite way, namely toward a layered perspective with models standing for abstractions of systems and programs.

The same fallacy is also behind the confusion between modeling and programming languages. That distinction is not arbitrary or based on languages peculiarities, but it fulfills a well-defined purpose: programming languages are meant to deal with software abstractions, while modeling languages take the broader perspective of systems.

#8: “Pie-in-the-sky” Meta-models

As any model, a meta-model is meant to map a concern with a context. But while models are concerned with the representation of business contexts, the purpose of meta-models is the processing of other models. Missing this distinction usually triggers a “flight for abstraction” and begets models void of any anchor to business relevancy. That may happen, for example, when looking for a meta-model unifying prescriptive and descriptive models; having very different aims, they belong to different realms and can never be joined by abstraction, but only by design.

#9: Problem/Solution Spaces

System engineering cannot be reduced to a simplistic dichotomy of problem and solution as it must solve three very different kinds of problems, with their respective contexts, stakeholders, and life-cycles:

  • Business ones, e.g how to assess insurance premiums or compute missile trajectory.
  • Functional ones, how the system under consideration should help solving business problems.
  • Operational ones, i.e how the system may achieve targeted functionalities for different users, within distributed locations, under economic constraints on performances and resources.

As it happens, and not by chance, those layers are congruent with modeling ones on one hand, architectural ones on the other hand.

#10: Enterprise Architecture is equivalent to Systems

Enterprise architecture is often confused with IT systems, which induces misguided understandings of business architecture. The key confusion here is between architectures, supposedly stable and shared, and processes, which are meant to change and adapt to competitive environments. But managing the dynamic alignment of assets (architecture capabilities) and supported business processes is at the core of enterprise architecture.

Further Reading

Representation (aka Functional) Patterns

Preamble

Whereas modeling languages like UML are just tools to be used to describe artifacts, the means often take precedence over the ends, as if the language telescope was used in reverse, looking at itself instead of targets. Representation patterns are an attempt to correct this bias by picking out features relevant to business requirements and system analysis before selecting language constructs to describe them.

Modeling patterns are reusable descriptions, i.e generic forms that can be used in different contexts. What is usually known as analysis patterns (business patterns may be more accurate) describe basic objects (customer, portfolio, …) or processes (take order, ship, invoice, …) independently of the way systems may support them. Design patterns describe system components independently of their business meanings.

Actual & Symbolic Realities (MC Escher)

Thanks to the Gang of Four, design patterns are widely accepted and consistently implemented across platforms. Yet, nothing equivalent has happened for analysis patterns, which remain confined to specific domains and organizations. That should have been expected considering that specificity and versatility are critical factors for business success: business models are not to be shared but are meant to change as fast as market opportunities. Hence, there is some gap between business and design patterns, namely, there is a need for representation  (aka analysis, aka functional) patterns, i.e generic descriptions of system functionalities independently of both their business meaning and system counterpart.

Beyond lexical controversies, that could be achieved with models and architectures defined along two parallel scales:

  • Architectures: enterprise (concepts), systems (functionalities), and platforms (technologies).
  • Models: conceptual (business context and organization), analysis (symbolic representations), design (physical implementation).

Interactions could be also symbolized with the “M” of model:

Descriptive (analysis) vs Prescriptive (design) Patterns

The rationale for representation patterns is double:

  • Since systems are defined as combinations of actual objects and processes on one hand, and their symbolic representations on the other hand, patterns of representations are clearly in need.
  • If models (and engineering) are to be driven by architecture, that must be supported by sound foundations, more precisely, it is necessary to map business requirements into functional archetypes.

Organization

Whereas some representation patterns are  sometimes described as analysis patterns, the terminology may introduce some confusion with business patterns. Following the rationale above, core patterns may be organized along two perspectives: representation and persistency.

Along the representation perspective, patterns must characterize how actual objects and processes are associated to their symbolic counterpart.

Symbolic representations and actual counterparts

Along the persistency perspective, patterns must characterize how execution units (i.e activities) are coupled to persistent ones. Depending on domains or architecture, patterns may describe processes synchronization or  time dependencies between persistent representations.

Engineering Symbolic Representations

Engineering processes are meant to sequence activities along intrinsic factors, as opposed to operational processes whose aim is to adapt activities to contexts. Whereas factors governing manufacturing processes depend upon the physical contingencies of material flows, the rationale behind software engineering processes is first and foremost governed by constraints on development flows, whose nature is essentially symbolic.

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Seats or Spades, Actual or Symbolic (Issa Samb).

Development processes usually follow one of two schools: procedural or acrobatic. Procedural processes impose one-fits-all development frameworks and significant overheads to compensate for the misfits; acrobatic ones bet on responsibility, expertise and best practices but don’t say much about development artefacts. The challenge is to take the best of each: shared understanding of model contents, lean developpment processes, sound anticipations, reliable commitments, and traceability.

Architecture Driven Modelling takes source from OMG’s Model Driven Architecture. More generally, it follows the well accepted distinction between requirements, analysis, and design, and can be implemented with OMG’s Unified Modeling Language.

Analysis models describe the symbolic representations of business objects and processes. They use requirement models, which describe business objects and processes. They are used by design models, which specify how symbolic representations will be implemented as system objects; implementation and deployment are not considered here.

Model Layers: Requirements, Analysis, Design.

Given those layers, system engineering has to manage objectives pertaining to a three-pronged perspective:

  • The business perspective is synchronic as it deals with the symbolic representation of context objects and processes. Since objectives, constraints, and risks change along their own time-frames, their symbolic representations must do the same; as a consequence system requirements must be continuously and consistently anchored to business  contexts.
  • The engineering perspective is diachronic as it deals with the implementation of system symbolic representation. Once rooted into requirements, the design and implementation of  symbolic representations are only concerned with the life cycle of development artefacts.
  • In between the architecture perspective is meant to be as invariant as core business concerns and corporate identities.

Given an architecture, both business and system models can be managed separately, each along its own timeframe, providing they don’t contradict architectural constraints.

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Actual & Symbolic Representations

Based upon the layered view of models,  engineering processes can be built bottom-up from work units directly defined from development flows. As a consequence, they can be better fitted to tasks, assessed, and improved.

Taking inspiration from the Capability Maturity Model Integration (CMMI), the benefits of architecture driven modelling can be identified for product, project, and process areas:

  • Traceability is obviously a starting point as it is a pre-requisite for streamlined engineering (product), portfolio and risk management (project), and application lifecycle management (process).
  • Measurement comes close, with built-in unbiased estimators, project workloads, and process assessment.
  • Quality management would clearly benefits from layered traceability and objective measurements, with built-in controls, non-regressive testing, and model-based validation.
  • Reuse provides another path to quality, with patterns (product), profiles (project) and development strategies (processes).
  • Finally, collaboration is to be facilitated between engineering processes targeting heterogeneous platforms, using different methodologies, across independent organizations.

Modeling Symbolic Representations

System modeling is all too often a flight for abstraction, when business analysts should instead look for the proper level of representation, ie the one with the best fit to business concerns.

Modeling is synchronic: contexts must be mapped to representations (Velazquez, “Las Meninas”).

Caminao’s blog (see Topics Guide) will try to set a path to Architecture Driven System Modelling. The guiding principle is to look at systems as sets of symbolic representations and identify the core archetypes defining how they must be coupled to their actual counterparts. That would provide for lean (need-to-know specs) and fit (architecture driven) models, architecture traceability, and built-in consistency checks.

This blog is meant to be a work in progress, with the basic concepts set open to suggestions or even refutation:

All examples are taken from ancient civilizations in order to put the focus on generic problems of symbolic architectures, disregarding technologies.

Symbolic representation: a primer

Original illustrations by Albert (http://www.albertdessinateur.com/) allow for concrete understanding of requirements, avoiding the biases associated with contrived textual descriptions.