The value of a model is its fitness to purpose. Missing this simple truth will inevitably trigger a “flight for abstraction” and begets models devoid of any anchor to business relevancy.
Abstraction ladders must be propped up by actual contexts (A. Magnaldo)
Yet, that pitfall can be avoided if requirements and models are put in perspective:
Requirements are meant to describe systems in their business context, models describe system artifacts. They should not be confused because the former are supposed to be rooted in concrete descriptions, while the latter aim at their abstract representation.
Models are built from nodes and associations. Nodes refer to instances which are supposed to be uniformly and consistently identified in both business and system contexts; associations may refer to instances (relationships and flows) or classes (specialization and generalization), the former with consistent semantics for business and system realms, the latter with semantics specific to system artifacts.
Along that perspective, the mapping of requirements into models can be achieved by applying selectively the two faces of abstraction: first removing information from the description of actual contexts, then building symbolic representations according to business concerns.
Business Context and Concerns
Starting with requirements, the challenge is therefore to move up and down the abstraction ladder until one gets the focus right, providing a clear and sharp picture of business context and concerns.
With regard to systems engineering, models’ semantics are unambiguously determined by their target: business environments or systems artifacts:
Models of business environments describe the relevant features of selected objects and behaviors, including supporting systems. Such models are said extensional as they target subsets of actual contexts.
Models of systems artifacts specify the hardware and software components of supporting systems. Such models are said intensional as they define artifacts to be created.
Business analysts figure models from actual contexts and concerns, software architects specify blueprints for artifacts
Climbing up the abstraction ladder, the objective is to align descriptive models of objects and behaviors with prescriptive models of system artifacts. That can be achieved in three steps:
Awareness of contexts: mind the business pie, drop everything else.
Domains of concern: say what features mean.
Symbolic representations: consolidate the descriptions of surrogates.
It is worth to note that the first two levels deal respectively with instances and features of actual objects and activities, while the third deal with artifacts. As a corollary, abstraction at the first two levels should be understood in terms of partitions and subsets, with subtypes introduced only for symbolic representations.
Awareness of Context
As illustrated by sounds (filtering noises) or optics (image point), focusing is a basic perceptual task targeting actual instances of objects, events, or activities. With regard to models, it can be achieved with a pronged move:
Adjust the depth of field to encompass the relevant business context. Large depths of field (aka deep focus) will cover concerns across domains, small ones (aka shallow focus) will support specific business concerns.
Single out image points for identified objects or activities deemed to be pivotal.
Field 1 is too small, field 3 is too large. “Dimensions” has no identity of its own, “Broom” is pointless.
A too shallow focus will capture only some of relevant objects (Piano), activities (Move) or events (Concert). Conversely, extending the focus may go too deep, including irrelevant items (Trumpet, Violinist, Illness, or Repair). Moreover, some image points may depend of others for their identity (Dimensions), or be pointless altogether (Broom).
Domains of Concerns
While business contexts are the same for all, business concerns are by nature specific to domains. The challenge for requirements capture is therefore to anchor specific features to shared objects and activities whose identities are set by business context.
For that purpose concerns are to be organized into domains responsible for the identification of anchors (objects, agents, activities) and the semantics of features:
Shared domains deal with anchors whose continuity and consistency have to be managed across domains, independently of activities.
Specific domains deal with anchors whose continuity and consistency can be managed within a single domain.
At this stage the challenge is to distinguish between identified instances of business objects (piano, concert) and processes (cleaning, moving, playing) on one hand, and the description of roles (mover, cleaner, pianist) and business logic (clean, move, play) on the other hand .
Domains of Concerns and Business Processes
It must be reminded that such models are still at ground level as they describe sets of instances; yet, they can also be seen as the first step up the abstraction ladder, as their objective is to extract relevant features and overlook others.
Symbolic Representations
While business concerns are partial and biased, the symbolic representations managed at system level must be comprehensive and consistent; that’s the objective of requirements analysis.
To start with, symbolic representations are introduced for each set of objects, roles or activities:
Objects surrogates: used to manage the continuity and consistency of business objects independently of business processes (piano, concert).
Process surrogates: used to manage the continuity and consistency of business operations independently of business objects (move, play, clean).
Roles: used to manage the interactions between actual agents and system functionalities (mover, cleaner, pianist). When the continuity and consistency of operations performed by agents are managed (e.g pianist), roles must be associated to surrogates.
Activities: used to describe business logic (move, play, clean).
Business logic and surrogates (#) for objects, processes and pianist .
Those are “flat” descriptions representing ground level instances. In order to be effectively supported by systems, models may have to be expanded downward by specialization, or upward by generalization.
Levels of Abstraction
As already noted, specialization and generalization are not symmetric because, contrary to the former operation, the latter one does modify the semantics of existing artifacts.
The purpose of specialization is to introduce specific descriptions for subsets of instances or features. For instance, assuming requirements are about moving pianos, the representation must climb one step down the abstraction ladder, from concerts to concerts with pianos:
Solo piano concerts are a subset of concerts subject to the same identification mechanisms and integrity constraints (strong inheritance).
The description of moving operations is not used to manage instances and its specialization is only about features (weak inheritance).
Climbing down: specialization of features (Move Piano) and surrogates (Solo Concert)
The purpose of generalization is twofold as it may be limited to features (aka aspect generalization) or targets both identification mechanisms and features (aka object generalization).
Aspect generalization introduces a base artifact for the description for shared features. Such base artifact is said to be abstract because its inheritance is limited to features and it cannot support the instantiation of surrogates, specialized artifacts keeping their own identification mechanisms. As a corollary, the level of abstraction is not modified because the model remains anchored to the same sets of instances. For instance (a), some administrative procedures can be defined uniformly for all maintenance operations otherwise described and executed independently.
Climbing up: generalization of features only (a), and for both features and individuals (b).
That’s not the case with object generalization which redefines the initial sets of surrogates as subsets of newly created super-sets. For instance (b), a cleaning process becomes a maintaining processes without the repair extension. Since maintaining processes can be created as simple cleaning or repairing ones, the model is anchored to different levels of abstraction. And since descriptions should not cross levels, roles must be specialized similarly: maintainers are to be identified as such before being qualified as mechanics, even if their interventions are not managed as such (inheritance of transient identification mechanism).
Getting A Proper Grip
Models are neither true or false and can only be assessed for consistency and effectiveness.
Hence, assuming that (1) systems are meant to handle surrogates of business objects and processes and, (2) those surrogates are designed from models, it ensues that (3) a litmus test of model effectiveness would be the grip it provides on relevant objects and processes.
A grip on context and concerns
And that can only be achieved by pinning models to concrete and identified business objects and processes. That provides a template for modeling grips: concrete descriptions with primary identification in the middle, abstract ones above, aspects or concrete descriptions with inherited identification below.
As long as information was just data files and systems just computers, the respective roles of enterprise and IT architectures could be overlooked; but that has become a hazardous neglect with distributed systems pervading every corner of enterprises, monitoring every motion, and sending axons and dendrites all around to colonize environments.
Enterprise Governance and Separation of Concerns (R.Magritte)
Yet, the overlapping of enterprise and systems footprints doesn’t mean they should be merged, as a matter of fact, that’s the opposite. When the divide between business and technology concerns was clearly set, casual governance was of no consequence; now that turfs are more and more entwined, dedicated policies are required lest decisions be blurred and entropy escalates as a consequence. The need of better focus is best illustrated by the sundry meanings given to “system”, from computers running software to enterprises running businesses, and even school of thought.
Concerns in Perspectives
As far as enterprises are concerned, systems combine human beings, devices, and software components.
From a functional perspective, their capabilities are best defined by their interactions with their environment as well as between their constituents:
Users are supposed to be actual agents granted with organizational status and responsibilities, and possessing symbolic communication capabilities.
Software components and actual devices cannot be granted with organizational status or responsibilities; the former come with symbolic communication capabilities, the latter without.
Assuming that interactions are governed by information, the objective is to understand the contribution of each type of component with regard to information processing and decision-making.
System = Agents + Devices + Symbolic representations
From an engineering perspective, the building of systems is all too often reduced to the development of their software constituents. As a consequence, the complexity of the forest is masked by the singularity of the trees:
The business value of applications is assessed locally instead of being driven by enterprise objectives and organizational constructs.
System models are confused with the programs used to produce software components.
Requirements life cycle, governed by the time-span of business contexts and objectives, is confused with the cycles of reuse of architecture assets.
Since engineering agenda are supposed to support business objectives, their decision-making processes must be aligned yet managed independently. That reasoning also applies to services management whose role is to adjust resources and software releases to operational needs.
Enterprise architectures can then be described as a cross between architecture assets (business objects, organization, technical architecture) on one hand, core processes for business, engineering and services management on the other hand.
EA Artifacts at crossroads between Architectures and Activities
Information is to provide the glue between architecture assets and supported processes.
Information and Architectures Levels
As understood by Cybernetics (see Stafford Beer, “Diagnosing the System for Organizations“), enterprises are viable systems whose success depends on their capacity to countermand entropy, i.e the progressive downgrading of the information used to govern interactions between systems and their environment. And that put knowledge management at the core of systems capabilities.
Knowledge is best defined as information put to use, with information obtained by adding references and sense to data. That blueprint is supposed to be repeated at all architecture levels:
At enterprise level facts pertaining to the conduct of business are captured from environments before being organized into information meant to support enterprise governance.
System level deals with symbolic descriptions of functionalities (information). At this level data is irrelevant, the objective being the consolidation and reuse of shared representations and patterns (knowledge).
The technical level is in charge of operational governance: software components, platforms capabilities, technical resources, communication mechanisms, etc. On one hand contexts are to be monitored and data translated into information; one the other hand operational decisions have to be made (knowledge) and executed which means information translated into data.
Architecture Layers and Processing Capabilities
If architecture levels can be characterized by processing capabilities, their engineering must be aligned to the corresponding objectives and time frames.
System Engineering and Separation of Concerns
As demonstrated time and again by blame games around projects failures, enterprise and technical concerns are poor engineering bedfellows, and with good reasons: different contexts, concerns, skills, and time scales. Faced with the challenge of bringing enterprise and development perspectives under a single governance, engineering approaches generally follow one of two basic options:
Phased projects (P) give precedence to software development, with requirements supposed to be set at inception and acceptance performed at completion.
Agile projects (A) give precedence to business requirements, with development iterations combining specifications, programming, and acceptance.
Phased (P) and Agile (A) development models
While each approach has its merits, agile for complex but well circumscribed projects, phased for large projects with external organizational dependencies, the difficulties each may encounter point to the crux of the matter, namely the separation of concerns between business goals and information technology, bypassed by agile approaches and misplaced by phased ones:
Agile development models are driven by users’ value and based on the implicit assumption that business and technology concerns can be dealt with continuously and simultaneously. That may be difficult when external dependencies cannot be avoided and shared ownership cannot be guaranteed.
Phased development models take a mirror position by assuming that business concerns can be settled upfront, which is clearly a very hazardous policy.
Those pitfalls may be overcome if engineering processes take into account the distinction between knowledge management on one hand, software development on the other hand.
Knowledge management (KM) encompasses all information pertaining to the conduct of business: environment, markets, objectives, organization, and projects. With regard to engineering projects, its role is to define and consolidate the descriptions of symbolic representations to be supported by information systems.
Software development (SD) starts with symbolic descriptions and proceeds with the definition, building and acceptance of the corresponding software artifacts.
Service management (SM) provides the bridge between engineering and operational processes.
Capture of information from data and legacy code can also be achieved respectively by data mining and reverse engineering.
System Engineering = Knowledge (KM) + Software (SD) + Service (SM)
Depending on organizational or technical dependencies, knowledge management and software development will be carried out within integrated development cycles (agile processes), or will have to be phased in order to consolidate the different concerns.
That engineering distinction neatly coincides with the functional divide of architectures: knowledge management supporting enterprise architecture, software development supporting system architecture.
Architectures and Engineering Processes
Business processes are governed by collective representations mixing goals, models and rules, not necessarily formally defined, and subject to change with opportunities. Engineering processes for their part have to be materialized through work units, models, and products, all of them explicitly defined, with limited room for change, and set along constraining schedules. Given that systems’ fate hangs on the hing between business and engineering concerns, the corresponding perspectives must be properly aligned.
That can be achieved if workshops are associated with architecture levels, and work units defined according model layers and development flows:
Knowledge management takes charge of symbolic descriptions pertaining to enterprise concerns. Its objective is to map business and operational requirements with the functionalities of supporting systems. Expressed in MDA parlance, the former would be described by computation independent models (CIMs), the latter by platform independent models (PIMs).
Software development deals with software artifacts. That encompasses the consolidation of symbolic descriptions into functional architectures, the design of software components according to platforms specificity (PSMs), and the production of code according to deployment targets (DPMs).
IT Service Management is the counterpart of knowledge management for actual operations and resources. Its objective is to synchronize business and development time-frames and align operational requirements with releases and resources.
Knowledge Management (KM), Software Development (SD), Services Management (SM)
That congruence between architecture divides (enterprise, systems, technology), models layers (CIMs, PIMs, PSMs, DPMs), and engineering concerns (facts or legacy, information, knowledge), provides a reasoned and comprehensive framework for enterprise architectures.
Architecture Capabilities & Separation of Concerns
Architectures describe stocks of shared assets, processes describe flows of changes. Given a hierarchized description of architectures, the objective is to ensure the traceability of concerns and decisions across levels and processes.
Who: enterprise roles, system users, platform entry points.
What: business objects, symbolic representations, objects implementation.
How: business logic, system applications, software components.
When: processes synchronization, communication architecture, communication mechanisms.
Where: business sites, systems locations, platform resources.
The rationale of objectives and decisions (the “Why” of Zachman framework) is expressed by dependency links according to the nature of primary factors:
Deontic dependencies are set by external factors, e.g regulatory context, communication technologies, or legacy systems.
Alethic (aka modal) dependencies are set by internal policies, based on the assessment of options regarding objectives as well as solutions.
Architecture Capabilities and Processes, with deontic (basic) and alethic (dashed) dependencies.
This distinction between dependencies is critical for decision-making and consequently for enterprise governance. Set within time-frames and decorated with time related features, those dependencies can then be consolidated into differentiated strategies for business, engineering and operational processes.
Given that dependencies are usually interwoven, governance must be aligned with the footprints of associated decisions:
Assets: shared decisions affecting different business processes (organization), applications (services), or platforms (purchased software packages).
Users’ Value: streamlined decisions governed by well identified business units providing for straight dependencies from enterprise (business requirements), to systems (functional requirements) and platforms (users’ entry points).
Non functional: shared decisions about scale and performances affecting users’ experience (organization), engineering (technical requirements), or resources (operational requirements).
Separation of Concerns and Requirements Taxonomy
That classification can be used to choose a development model:
Agile approaches should be the option of choice for requirements neatly anchored to users’ value.
Phased approaches should be preferred for projects targeting shared assets across architecture levels. When shared assets are within the same level epic-like variants of agile may also be considered.
Architectures, Processes, and Governance
While there is some consensus about the scope and concerns of Enterprise Architecture as a discipline, some debate remains about the relationship between enterprise and IT governance.
Beyond turf quarrels, arguments are essentially rooted in the distinction between information and supporting systems or, more generally, between organization and IT.
To some extent, those arguments can be ironed out if governance were set with regard to scope, actual or symbolic:
Actual scope deals with current or planned business objects, assets, and processes.
Symbolic scope deals with the design of the corresponding software components.
What is to be governed: actual and symbolic scopes
That distinction matches the divide between enterprise and systems architectures: one set of models deals with enterprise objectives, assets, and organization, the other one deals with system components.
With regard to processes, governance should distinguish between business and engineering:
Business processes are clearly designed at enterprise level, both for their organization and the symbolic description of system representations.
Software engineering ones are under the responsibility of IT governance, from system and software architecture to release and deployment.
Process and architecture perspectives for Governance
While governance could be shared, there is no reason to assume immediate and continuous alignment of perspectives; that could only be achieved through architecture perspective:
Actual architectures encompass business organization (locations, agents, devices) and systems deployments.
Symbolic architectures include systems functionalities and software development.
And, as a mirror achievement, processes take charge of transitions between actual and symbolic architectures:
Processes carry out Architectures alignment (Pb,Pe), Architectures support Processes consistency (As, Ap).
Symbolic architectures provide the mediation between business requirements and software development (As).
Physical architectures do the same between software development and services management (Ap).
Business processes take responsibility for the mapping of enterprise architecture into symbolic representations (Pb).
Engineering processes do the same for the alignment of software and deployment architectures (Pe).
Enterprise and IT governance can then be defined in terms of Knowledge management, with engineering and business processes gathering data from their respective realms, sharing the information through architectures, and putting it to use according their respective concerns.
As illustrated by he Symbolic Systems Program (SSP) at Stanford University, advances in computing and communication technologies bring information and knowledge systems under a single functional roof, namely the processing of symbolic representations.
Information and Knowledge: Acquisition, Use and Reuse (R. Doisneau)
Within that understanding one will expect Knowledge Management to shadow systems architectures and concerns: business contexts and objectives, enterprise organization and operations, systems functionalities and technologies. On the other hand, knowledge being by nature a shared resource of reusable assets, its organization should support the needs of its different users independently of the origin and nature of information. Knowledge Management should therefore bind knowledge of architectures with architecture of knowledge.
Knowledge Representation
In their pivotal article Davis, Shrobe, and Szolovits set five principles for knowledge representation:
Surrogate: KR provides a symbolic counterpart of actual objects, events and relationships.
Ontological commitments: a KR is a set of statements about the categories of things that may exist in the domain under consideration.
Fragmentary theory of intelligent reasoning: a KR is a model of what the things can do or can be done with.
Medium for efficient computation: making knowledge understandable by computers is a necessary step for any learning curve.
Medium for human expression: one the KR prerequisite is to improve the communication between specific domain experts on one hand, generic knowledge managers on the other hand.
Surrogates without Ontological Commitment
That puts information systems as a special case of knowledge ones, as they fulfill the five principles, yet with a functional qualification:
The only difference is about coupling: contrary to knowledge systems, information and control ones play a role in their context, and operations on surrogates are not neutral.
Knowledge Archeology
Knowledge constructs are empty boxes that must be properly filled with facts. But facts are not given but must be 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. Taking wind as an example, wind socks support immediate observation of facts, free of any symbolic meaning. In order to make sense of their behaviors, wanes and anemometers are necessary, respectively for azimuth and speed; but that also requires symbolic frameworks for directions and metrics. Finally, knowledge about the risks of strong winds can be added when such risks must be considered.
Fact, Information, Knowledge
As far as enterprises are concerned, knowledge boxes are to be filled with facts about their business context and processes, organization and applications, and technical platforms. Some of them will be produced internally, others obtained from external sources, but all should be managed independently of specific purposes. Whatever their nature (business, organization or systems), information produced by the enterprises themselves is, from inception, ready to use, i.e organized around identified objects or processes, with defined structures and semantics. That’s not necessarily the case with data reflecting external contexts (markets, regulations, technology, etc) which must be mapped to enterprise concerns and objectives before being of any use. That translation of data into information may be done immediately by mapping data semantics to identified objects and processes; it may also be delayed, with rough data managed as such until being used at a later stage to build information.
From Data to Knowledge
From Data to Information
Information is meaningful, data is not. Even “facts” are not manna from heaven but have to be shaped from phenomena into data and then information, as epitomized by binary, fragmented, or “big” data.
Binary data are direct recording of physical phenomena, e.g sounds or images; even when indexed with key words they remain useless until associated, as non symbolic features, to identified objects or activities.
Contrary to binary data, fragmented data comes in symbolic guise, but as floating nuggets with sub-level granularity; and like their binary cousin, those fine-grained descriptions are meaningless until attached to identified objects or activities.
“Big” data is usually understood in terms of scalability, as it refers to lumps too large to be processed individually. It can also be defined as a generalization of fragmented data, with identified targets regrouped into more meaningful aggregates, moving the targeted granularity up the scale to some “overwhelming” level.
Since knowledge can only be built from symbolic descriptions, data must be first translated into information made of identified and structured units with associated semantics. Faced with “rough” (aka unprocessed) data, knowledge managers can choose between two policies: information can be “mined” from data using statistical means, or the information stage simply bypassed and data directly used (aka interpreted) by “knowledgeable” agents according to their context and concerns.
Non symbolic data can be interpreted by “knowledgeable” agents according to their concerns.
As a matter of fact, both policies rely on knowledgeable agents, the question being who are the “miners” and what they should know. Theoretically, miners could be fully automated tools able to extract patterns of relevant information from rough data without any prior information; practically, such tools will have to be fed with some prior “intelligence” regarding what should be looked for, e.g samples for neuronal networks, or variables for statistical regression. Hence the need of some kind of formats, blueprints or templates that will help to frame rough data into information.
Information Properties
Knowledge must be built from accurate and up-to-date information regarding external and internal state of affairs, and for that purpose information items must be managed according to their source, nature, life-cycle, and relevancy:
Source: Government and administrations, NGO, corporate media, social media, enterprises, systems, etc.
Nature: events, decisions, data, opinions, assessments, etc.
Type of anchor: individual, institution, time, space, etc.
Life-cycle: instant, time-related, final.
Relevancy: traceability with regards to business objectives, business operations, organization and systems management.
Information must be timely, understandable, and relevant
On that basis, knowledge management will have to map knowledge to its information footprint in terms of reliability (source, accuracy, consistency, obsolescence, etc) and risks.
From Information to Knowledge
Information is meaningful, knowledge is also useful. As information models, knowledge representations must first be anchored to persistency and execution units in order to support the consistency and continuity of surrogates identities (principle #1). Those anchors are to be assigned to domains managed by single organizational units in charge of ontological commitments, and enriched with structures, features, and associations (principle #2). Depending on their scope, structure or feature, semantics are to be managed respectively by persistent or application domains. Likewise, ontologies may target objects or aspects, the former being associated with structural sub-types, the latter with functional ones. Differences between information models and knowledge representation appear with rules and constraints. While the objective of information and control systems is to manage business objects and activities, the purpose of knowledge systems is to manage symbolic contents independently of their actual counterparts (principle #3). Standard rules used in system modelling describe allowed operations on objects, activities and associated information; they can be expressed forward or backward:
Forward (aka push) rules are conditions on when and how operations are to be performed.
Backward (aka pull) rules are constraints on the consistency of symbolic representations or on the execution of operations.
Standard Rules
Assuming a continuity between information and knowledge representations, the inflection point would be marked by the introduction of modalities used to qualified truth values, e.g according temporal and fuzzy logic:
Temporal extensions will put time stamps on truth values of information.
Fuzzy logic put confidence levels on truth values of information.
That is where knowledge systems depart from information and control ones as they introduce a new theory of intelligent reasoning, one based upon the fluidity and volatility of knowledge.
Meanings are in the Hands of Beholders
Seen in a corporate context, knowledge can be understood as information framed by contexts and driven by purposes: how to run a business, how to develop applications, how to manage systems. Hence the dual perspective: on one hand information is governed by enterprise concerns, systems functionalities, and platforms technology; on the other hand knowledge is driven by business processes, systems engineering, and services management.
Knowledge of Architectures, Architecture of Knowledge.
That provides a clear and comprehensive taxonomy of artifacts, to be used to build knowledge from lower layers of information and data:
Business analysts have to know about business domains and activities, organization and applications, and quality of service.
System engineers have to know about projects, systems functionalities and platform implementations.
System managers have to know about locations and operations, services, and platform deployments.
The dual perspective also points to the dynamics of knowledge, with information being pushed by the their sources, and knowledge being pulled by their users.
A Time for Every Purpose
As understood by Cybernetics, enterprises are viable systems whose success depends on their capacity to countermand entropy, i.e the progressive downgrading of the information used to govern interactions both within the organization itself and with its environment. Compared to architecture knowledge, which is organized according to information contents, knowledge architecture is organized according to functional concerns and information lifespan, and its objective is to keep internal and external information in synch:
Planning of business objectives and requirements (internal) relative to markets evolutions and opportunities (external).
Assessment of organizational units and procedures (internal) in line with regulatory and contractual environments (external).
Monitoring of operations and projects (internal) together with sales and supply chains (external).
Knowledge Architecture and Shearing Layers: strategy at leisure, time for plans, real-time operations.
That put meanings (that would be knowledge) in the hands of decision makers, respectively for corporate strategy, organization, and operations. Moreover, enterprises being living entities, lifespan and functional sustainability are meant to coalesce into consistent and homogenous layers:
Enterprise (aka business, aka strategic) time-scales are defined by environments, objectives, and investment decisions.
Organization (aka functional) time-scales are set by availability, versatility, and adaptability of resources
Operational time-scales are determined by process features and constraints.
Such a congruence of time-scales, architectures and purposes into Shearing Layers is arguably a key success factor of Knowledge management.
Search and Stretch
As already noted, knowledge is driven by purposes, and purposes, not being confined to domains or preserves, are bound to stretch knowledge across business contexts and organizational boundaries. That can be achieved through search, logic, and classification.
Searches collect the information relevant to users concerns (1). That may satisfy all the knowledge needs, or provide a backbone for further extension.
Searches can be combined with ontologies (aka classifications) that put the same information under new lights (1b).
Truth-preserving operations using mathematics or formal languages can be applied to produce derived information (2).
Finally, new information with reduced confidence levels can be produced through statistical processing (3,4).
For instance, observed traffic at toll roads (1) is used for accounting purposes (2), to forecast traffic evolution (3), to analyze seasonal trends (1b) and simulate seasonal and variable tolls (4).
Those operations entail clear consequences for knowledge management: As far as computational distances don’t affect confidence levels, truth-preserving operations are neutral with regard to KM. Classifications are symbolic tools designed on purpose; as a consequence all knowledge associated to a classification should remain under the responsibility of its designer. Challenges arise when confidence levels are affected, either directly or through obsolescence. And since decision-making is essentially about risks management, dealing with partial or unreliable information cannot be avoided. Hence the importance of managing knowledge along shearing layers, each with its own information life-cycle, confidence requirements, and decision-making rules.
From Knowledge Architecture to Architecture Capability
Knowledge architecture is the corporate central nervous system, and as such it plays a primary role in the support of operational and managerial processes. That point is partially addressed by Frameworks like Zachman whose matrix organizes Information System Architecture (ISA) along capabilities and design levels. Yet, as illustrated by the design levels, the focus remains on information technology without explicitly addressing the distinction between enterprise, systems, and platforms.
Capabilities can be defined across architecture layers with regard to business, engineering, and operational processes
That distinction is pivotal because it governs the distinction between corresponding processes, namely business processes, systems engineering, and services managements. And once the distinction is properly established knowledge architecture can be aligned with processes assessment.
Yet that will not be enough now that digital environments are invading enterprise systems, blurring the distinction between managed information assets and the continuous flows of big data.
The outer range anchors enterprise architecture and objectives to business environments
That puts the focus on two structural flaws of enterprise architectures:
The confusion between data, information, and knowledge.
The intrinsic discrepancy between systems and knowledge architectures.
Both can be overcame by merging system and knowledge architectures applying the Pagoda blueprint:
Pagoda Architecture Blueprint
The alignment of platforms, systems functionalities, and enterprise organization respectively with data (environments), information (symbolic representations), and knowledge (business intelligence) would greatly enhance the traceability of transformations induced by the immersion of enterprises in digital environments.
Knowledge Representation & Profiled Ontologies
Faced with digital business environments, enterprise must sort relevant and accurate information out of continuous and massive inflows of data. As modeling methods cannot cope with the open range of contexts, concerns, semantics, and formats, looser schemes are needed, that’s precisely what ontologies are meant to do:
Thesaurus: ontologies covering terms and concepts.
Documents: ontologies covering documents with regard to topics.
Business: ontologies of relevant enterprise organization and business objects and activities.
Engineering: symbolic representation of organization and business objects and activities.
Ontologies: Purposes & Targets
Profiled ontologies can then be designed by combining that taxonomy of concerns with contexts, e.g:
Institutional: Regulatory authority, steady, changes subject to established procedures.
Professional: Agreed upon between parties, steady, changes subject to accords.
Corporate: Defined by enterprises, changes subject to internal decision-making.
Social: Defined by usage, volatile, continuous and informal changes.
Personal: Customary, defined by named individuals (e.g research paper).
Last but not least, external (regulatory, businesses, …) and internal (i.e enterprise architecture) ontologies could be integrated, for instance with the Zachman framework:
Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).
Using profiled ontologies to manage enterprise architecture and corporate knowledge will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).
An ontological kernel has been developed as a Proof of Concept using Protégé/OWL 2; a beta version is available for comments on the Stanford/Protégé portal with the link: Caminao Ontological Kernel (CaKe).
From Data Analysis to Deep Learning
Set between all-inclusive onslaught of data on one side, pervasive smart bots on the other side, information systems could lose their identity and purpose. And there is a good reason for that, namely the confusion between data, information, and knowledge.
Knowledge is the ability to make differences
As it happened aeons ago, ontologies have been explicitly though up to deal with that issue.
Whereas UML has been brought to existence by very wise men under very propitious skies, the initial enthusiasm and first successes have never been transformed into wider acceptance and customary usage; subsequent updates and extensions didn’t help and may even have triggered some anticlimax. More than fifteen years after its launch, the utilization of UML remains limited, both in breadth (projects developed) and depth (features effectively used). Moreover, the UML house is deeply divided and there isn’t much consensus among the few that use it comprehensively and consistently, principally to support domain specific languages (DSL).
The Divided House of UML (Anne Desmet)
Certainly, there must have been a wrong turn somewhere, possibly at the UML2 crossing when the OMG committee lost sight of users modeling needs and took the road to meta-models. Considering UML’s shrinking stamp and dwindling relevancy, that road appears more and more like a dead-end; but it may be still possible to get back on track and retrieve the Us of the UML: unified semantics for all and sundry users.
Where to Look
Whether on driving or back seats, respectively for model driven or agile methods, models are widely accepted as a necessary constituent of development processes. Nonetheless, and despite being the only official standard, UML standing appears to falter, up to be already seen as a cold case. As suggested by Ivar Jacobson (“The road ahead for UML“), one of its main drawback would be its lack of modularity with regard of users needs. If that flaw is to be fixed, the question is where to look: directly at language level, or at supporting mechanisms.
Given the broad consensus that surrounded the initial project, one should at first look for a sound and stable subset to be used as a backbone and fleshed out according specific contexts, purposes or users. As a matter of fact that is what stereotypes and profiles are meant to do, except that without a well-defined backbone of unambiguous constructs, the only possible outcomes are domain specific languages. So, one should first consider how the separation of concerns could be better supported by language constructs.
Language Constructs, Model, and Separation of Concerns
Separation of Concerns
Despite its roots in the Object Oriented paradigm, UML has demonstrated its adaptability to all and every method or domain. Unfortunately, being a Jack of all trades often means a master to none, and the use of UML is clearly frustrated by its versatility; that translates either into shallow usage of ambiguous semantics, or into extensions targeting specific domains or technologies.
On the ground, three mechanisms can be used to make for the lack of focus: stereotypes, views, and customization.
UML stereotyping mechanism support predefined constructs for problem (business objects and processes) or solution (system architecture and object design) spaces. Stereotypes can be grouped into profiles, e.g for specific business domains or technical architectures.
Views (or perspectives) organize access to models according contents: logical, physical, conceptual, pragmatic, etc
Tool customization organizes access to models according users purposes and skills: analyst, architect, designer, developer, etc.
While those approaches have their benefits, they are set independently of languages constructs, either as UML extensions (stereotypes and profiles), or defined from outside by development methodologies (views) or projects organization (customization). As a consequence, they have little or no effect on the simplicity or efficiency of UML; they may even add to confusion and complexity when overlapping stereotypes are introduced to support multiple taxonomies, e.g technical architectures and business domains.
Language Constructs (a), Stereotyped Model (b), Combined Views or Profiles (c)
That may point to a clear direction: given the potency of the stereotyping mechanism and its pivotal role in UML utilization, significant benefits could be achieved through a better integration into core language constructs, even if that entails some constraints or limitations. Two straight modifications should be considered:
Model layers: language constructs should be re-organized along architectural concerns for enterprise (business processes), system (functionalities), and platforms (components).
Stereotypes visibility: language constructs should support the distinction between local taxonomies and “unified” ones, the former set with limited scope and visibility, the latter meant to be applies across layers.
While both modifications can be carried out on their own, their benefits would be boosted if they were set within the broader MDA framework and supported by specific language constructs.
Modular Language Constructs
Given the growing intricacy, ubiquity and diversity of systems, UML complexity and versatility should clearly be in demand, and the problem is to harness those capabilities according the needs and skills of the different kinds of users.
That’s arguably a critical flaw of UML, which lumps together essential with secondary constructs, as well as definite with ambivalent semantics. That brings weighty consequences, both for users and models:
Steep or even abrupt learning curve: confronted to a wall of mixed constructs users have to master the whole upfront, whatever their needs and skills.
Blurred concerns: describing various specific contents with the same ambivalent constructs will either distort language semantics, or blur concerns specificities.
Corrupted transformation: whatever the modeling tools, the bad apples of inputs will usually corrupt the whole of outputs. In other words any advance in model driven development requires a sound backbone of unambiguous language constructs.
As noted above, language constructs can be regrouped along two perspectives, one directly associated with users architectural concerns, the other reflecting the scope and visibility of targeted artifacts. While there is no particular reason to match complexity levels with architectural concerns, mapping them to granularity has a clear rationale. Such a “born again” UML would distinguish between two levels of language constructs:
Those pertaining to objects and activities identified by architectures, whatever their nature: enterprise, systems or platforms.
Those used to describe internals of objects and activities independently of their aspects and behaviors at architecture level.
Model Transformation: lumped (b) vs differentiated (a) language constructs.
That re-configuration would bring modularity to the language, enabling a smooth learning curve. More importantly, a clear-cut separation of concerns will enable some kind of Just-In-Time model transformation: instead of cumulative noises (b), one will get separate transformations for models architectural backbone on one hand, contingent specificities on the other hand (a). And that could be a real game-changer for lean and fit models.
While that could be achieved by different means, a simple solution would be to use the stereotyping mechanism to describe supporting structures of enterprise, functional, and technical architectures.
Transformation vs Portability
Model transformation is about changing contents within the same environment, portability is about moving the same contents across different environments; and despite apparent similarities, they deal with different concerns, set by users for the former, by tools vendors for the latter.
Transformation is normally performed under a single corporate roof according agreed semantics; as a corollary, it is meant to cover the full contents of models. That’s not the case for portability, whose primary objective is the exchange of consolidated contents between heterogeneous environments; while sources and targets may have to share the whole of their models, a sound policy should make room for selective portability of specific or confidential contents.
The Meta-Object Facility (MOF) is the solution of choice for portability. As a meta-language it is used to describe language constructs at source and target environments; mapping rules can then be defined and bridges built between environments. As it is, those bridges usually scale very poorly due to the exponential complexity of rules having to cover all and every model idiosyncrasies; and that’s unfortunate for portability which, instead of focused targets, has to deal with overweight models cluttered with useless contents (b).
Portability between modeling environments: Lumped (b) vs Differentiated (a) constructs.
That situation would be greatly improved (a) if the wheat of consolidated constituents could be separated from the chaff of ambiguous or irrelevant contents. On a broader perspective that will open the way leaner and fitter models.
One step back may put UML back on track
There is something of a consensus among the software engineering community regarding (1) the benefits of models and (2) the failures of UML. As should be expected, that consensus translates into fragmented modeling practices and, more generally, software engineering methodologies. Obviously there isn’t much of a future for UML along that path, but the case is still open and the trend can be reversed by putting users needs back on UML driving seat.
Model driven software engineering (aka MDA, aka MBSE, aka MDE) has had a great future for quite some time, yet there isn’t much consensus about what that could be and, in particular, about what kind of models should be in the driving seat.
Shadowing Reality
Pending some agreement about model contents (e.g specific ?) and capabilities (e.g executable ?), the driving of software engineering processes will probably remain more practices than principles.
Shadowing Reality
Models are shadows of reality, with their form and contents set by contexts and concerns. They can be characterized by their purpose and capabilities.
Regarding purpose, models fall in two groups: descriptive models deal with problems at hand, prescriptive models with solutions.
Descriptive models are partial and biased representations of actual contexts. Partial because they only deal with relevant objects, activities and features; biased because the selection is made on purpose.
Prescriptive models are complete descriptions of artifacts.
Regarding capabilities the distinction is between intensional and extensional languages:
Extensional (aka denotative) languages deal with sets of identified instances of objects and activities. As they condone partial or ambiguous statements, they are best used for descriptive models.
Intensional (aka connotative) languages deal with the semantics and constraints of symbolic representations. Due to their formal capabilities they are best used for prescriptive models.
Along that reasoning System Models can be characterized along architecture contexts: business processes (enterprise), functionalities (systems), and platform implementations (technologies):
Business models are descriptive and built with extensional languages (business is often said to bloom on discrepancies). They are necessarily partial as they target specific contexts and concerns.
Functional (aka analysis) models are prescriptive and built with intensional languages as they must specify the semantics and constraints of symbolic representations. Yet they are not necessarily complete since they don’t have to cover every details of business processes or implementations (cf traceability).
Implementation models (aka design) are prescriptive with no use for extensional capabilities since the relevant physical objects, i.e extensions, are system components directly derived from system specifications. However, they must support complete and formal descriptions of component features.
Business object, analysis and design, implementation.
Models don’t Talk Alone
Models are built with logographic languages that should not be confused with phonetic ones: whereas the latter convey information sequentially, the former build semantics from different sources; that enables models to be read from different perspectives. Contrary to programs whose semantic is bound to a fixed sequential execution, models don’t talk alone, but must be chatted to. Even when their readings are sequential, the sequences are governed by readers, not by models.
That point is pivotal if model transformation, arguably a pillar of model driven development, is to be supported along different perspectives and according different concerns.
Besides, it must be noted that while models can be fully translated into (and reversed from) sequential representations (e.g with XMI), transcripts are just projections and should not be confused with models as such.
Models don’t Walk Alone
Like talks, walks are sequential as they advance step by step. Hence, while some models can be walked (aka executed), such walks are only instances of behaviors supported by models.
That should be especially clear for system models which describe architectures combining agents and devices as well as software components, contrary to programs which are limited to software components structure and sequential (or parallel) execution.
Just like XMI transcripts should not be confused with original models, “executable” models should not be confused with fully fledged ones because they simulate the behaviors of agents and devices as if they were software components. While that may be useful for models targeting software components within a given architecture, ignoring the specificities of software, agents, and devices would be pointless when the objective is to design a system architecture.
Abstraction Levels
Defining models as abstractions may be correct but not very helpful when deciding what kind of abstraction should drive development processes. First, the question is to define how concerns and purposes should be used to define abstractions, in other words to set apart significant from non-significant information. Then, in order to avoid flights for always higher abstractions without burying models into ground details, some principles are needed regarding specialization and generalization.
When systems are considered, abstraction levels are set by enterprise organization, systems functionalities, and platform technologies, with concerns expressed by business, functional, or technical requirements. Given a hierarchy of concerns, models must be set at the proper level of abstraction: below that level part of information will be redundant or irrelevant; above, useful features and classifications may be overlooked as some information will be either wanting or will not discriminate between variants.
Such levels can be identified by selectively applying specialization and generalization to constrained hierarchies:
Inheritance should not cross model layers: hierarchies of business, functional and technical artifacts must be kept separate.
Structures and behaviors pertain to different concerns: abstractions of objects and aspects should be managed independently.
Specialization should be applied when subset of identified objects or behavior are associated with specific features. Generalization should be introduced when models must be consolidated.
Such an approach brings significant benefits for reuse, arguably one of the main objective of abstraction. And that appears clearly when developments are governed by models and architecture components and mechanisms are to be shared across model layers.
Reuse of business and functional models along functional tiers, with abstractions for structures (green) and aspects (orange).
Driving Models and Roadmaps
Systems engineering has to meet three kinds of requirements: business needs, system functionalities, and components implementation. In a perfect world there would be one stakeholder, one architecture, and one time-scale. Unfortunately, projects may involve different stakeholders, target different architectures, and be set along different time-frames. In that case milestones and roadmaps are to be introduced in order to bring all expectations and commitments under a common roof, with models providing the glue between them. That may be achieved with models defined along MDA layers:
Computation Independent Models (CIMs) describe business objects and processes independently of supporting systems.
Platform Independent Models (PIMs) describe how business processes are supported by systems seen as functional black boxes, i.e disregarding the constraints associated to candidate technologies.
Platform Specific Models (PSMs) describe system components as implemented by specific technologies.
From Models to Roadmaps
Interestingly, both extremities of development processes are textual descriptions, informal at the beginning, formal at the end, with models providing bridges in between. As noted above, those bridges are not always necessary: texts can be directly translated into instructions (as illustrated by voice command devices), or project teams with shared ownership can develop programs according users’ requirements (as promoted by Agile methods). Yet, the question should always be asked, and when textual requirements cannot be directly developed into programs the first task should be to draw the shortest modeling path.
Roadmaps and Meta-models
Model driven tools may appear under different guises yet most rely on meta-models and the Meta Object Facility (MOF). Given that meta-models are models of models, they are supposed to focus on a subset of relevant features selected on purpose, which, for driving models, would be some kind of road signs governing models transformation. What that could be? Two approaches are to be considered:
Language translation: as presented by the report of the Dagstuhl Seminar, meta-models can be designed according their generic transformation capabilities and used to single out language constructs in order to transfer model contents into another language.
Separation of concerns: as development advances and models take into account different concerns, meta-models can be used to monitor and control the selective processing of corresponding contents. That could be achieved if transformations were governed by traffic signs singling out relevant features and waving aside the leftovers.
Each option points to a different perspective, the former targeting tools providers, the latter aiming at modellers. Whereas MDA layers (for business, functionalities, and technology) clearly point to models built with the Unified Modeling Language according organizational, functional and technical concerns, most of current implementations follow the language option; while those tools may be (theoretically) open at technical level, they usually rely on domain specific languages. By narrowing functional scopes and relying on automated translation to bridge the gaps, solutions based upon domain specific languages can only provide local solutions to fragmented problems. That road could be a bumpy one for model driven engineering.
Postscript
Thinking again, the “MDA” moniker can be misleading as it may blur the distinction between models and their contents: given that MDA model layers effectively correspond to architecture levels, the pivotal MDA contention is that the modeling of systems is meant to be driven by enterprise architecture divides.
Reusing artifacts means using them in contexts that are different of their native ones. That may come by design, when specifications can anticipate on shared concerns, or as an afterthought, when initially unexpected similarities are identified later on.
Economics of Reuse (Sergio Silva)
Planned or opportunistic, reuse brings benefits in terms of costs, quality, and continuity:
Cost benefits are most easily achieved for component engineering but may also be obtained upstream with model reuse and patterns.
Quality benefits are first and foremost rooted at model level, especially when components implementation is supported by automated tools.
Continuity benefits are to be found both along the business (semantics and business rules) and engineering (functional architecture and platform implementations) perspectives.
Reuse policies may also bring positive externalities by inducing a comprehensive approach to software design.
Yet, those policies will usually entail costs and may as well bear negative externalities:
Artifacts designed for reuse are usually most costly to develop, even if part of additional costs should be ascribed to quality management.
Excessive enforcement policies may significantly hamper projects ability to meet business needs in time.
Managing reusable assets usually induces overheads.
In order to assess those policies, economics of reuse must be set across business, engineering or architecture perspectives:
Business perspective: how to factor out and reuse artifacts associated with the knowledge of business domains when system functionalities or platforms are modified.
Engineering perspective: how to reuse development artifacts when business contents or system platforms are modified.
Architecture perspective: how to use system components independently of changes affecting business contents or development artifacts.
That can be achieved by managing development assets along model driven architecture: CIMs for business and enterprise architecture, PIMs for systems functional architecture, and PSMs for systems technical architecture.
Contexts & Concerns
Whatever their inception, reused artifacts are meant to be used independently of their native context and concerns: opportunistic reuse will map a specific purpose to another one, planned reuse will map a shared concern to a specific purpose. As a corollary, reuse policies must be supported by some traceability mechanism linking concerns and purposes across contexts and architectures.
From the enterprise perspective, the problem is to reuse the knowledge of business domains and processes. For that purpose different mechanisms can be considered:
The simplest solution is to reuse generic components, with the business knowledge directly transferred through parameters.
Similarly, business rules can be separately edited and managed in business contexts before being executed by system components.
One step further, business semantics and rules can be fenced off with domains and applied to different objects and applications.
Finally, models of business objects and processes can be capitalized and managed as reusable assets.
Reuse Policies with Model Driven Architecture
Once business knowledge is duly capitalized as functional assets, they can be reused along the engineering perspective:
System functionalities: functional patterns are (re)used to map functional requirements to functional architecture, and services are (re)used to support business processes.
Software implementations: Component-based development and information hiding.
Along the architecture perspective information hiding is generalized to systems, and reuse is masked by the definition of services.
It must be noted that, contrary to misleading similarities, refactoring is the opposite of reuse: instead of building from well understood and safe artifacts, it tries to extract some reusable chunks from opaque and unsafe components.
Knowledge Reuse
Enterprise and business knowledge may affect the full scope of system functionalities: boundaries (e.g users authorizations), controls (e.g accounting rules), and persistency (e.g consistency constraints). Whereas there isn’t much to argue about the benefits of reusing enterprise and business knowledge, costs may significantly diverge depending on the way corresponding assets are managed:
Domain specific knowledge are rooted at requirements level. That’s typically achieved when use cases are introduced to describe how systems are meant to support business processes. With different use cases targeting different aspects of the same business objects and processes, overlaps must be identified and factored out in order to be reused across processes.
Business knowledge may also be global, i.e shared at enterprise architecture level, defining objectives, assets and organization associated with the continuity of corporate identity and business capabilities within a regulatory and market environment.
Use cases access to respectively shared (a,b) and specific (c,d) knowledge for objects and application domains
In any case, the challenge is to map business knowledge to system models, more precisely to embed reused descriptions of business objects and process to corresponding development artifacts. At architecture level the mapping should target objects or processes identities, at domain level the focus will be on aspects and views.
As epitomized by service oriented architectures, business architectures can be mapped to system ones through delegation, either directly (business processes calling on services), or indirectly (collaboration between services). That will establish a clear distinction between shared (aka global) and domain specific knowledge, and consequently between respective economics of reuse.
Given that shared knowledge must be reused across domains and applications, there can be no argument about benefits. That will be achieved by a messaging model built on a need-to-know basis. And since such model is an intrinsic feature of the functional architecture, it incurs no additional overheads.
Specific knowledge for its part is managed at domain level and therefore masked behind services interfaces. Whatever reuse occurs there remains local and an intrinsic part of domain design.
Knowledge Reuse through services (a), boundaries (b), controls (c), and entities (d).
Things are not so clear when business knowledge is not managed by services but distributed across domains, mixing specific and global knowledge. Managing reusable assets would be easy were the distinction between business and functional requirements to coincide with the one between shared and specific knowledge; unfortunately that’s seldom the case, and requirements, functional or business, will have to be sorted out at architecture or design level.
Whereas Service Oriented Architectures (SOA) put functionalities in the driving seat, Enterprise Application Integration (EAI) gives the lead to applications for which it provides adapters. As maintenance of integration adapters is a very poor substitute for knowledge management, reuse is mostly limited to legacy applications.
Maintaining adapters across layers of applications induces significant overheads
At design level knowledge is weaved into canonical data models (entities), functional architectures (controls), and user interfaces frameworks (boundaries).
Mixed concerns: business requirements can be specific, functional ones can be global.
On one hand, tracing reuse to requirements may be problematic as they are by nature concrete and unstructured, hence not the best support for generalization or the factoring out of shared features. Assuming business analysts can nonetheless separate reusable wheat from specific chaff, knowledge management at this level will require a dedicated framework supporting comprehensive and differentiated traceability. Additional overheads will have to be taken into account and compared to potential benefits.
On the other hand, canonical data models and functional architectures are meant to provide unified views of shared objects and semantic domains. Yet, canonical models are by nature unwieldy as they carve structures, features, and connections of business objects, without clear mechanisms to combine shared and specific knowledge. As a corollary, their use may reduce flexibility, and their management usually induces significant overheads.
Artifacts Reuse
With enterprise and business knowledge capitalized as development assets, the engineering case for reuse may appears indisputable, but business cases are often much more controversial due to large overheads and fleeting returns. Taking cues from Barry Boehm (“Managing Software Productivity and Reuse”), here are some of the main pitfalls of artifacts reuse:
Repository delusion: knowledge being by nature contextual, its reuse is driven by circumstances and purposes; as a consequence the availability of large repositories of development assets will probably be ignored without clear pointers rooted in contexts and concerns.
Confusion between components (or structures) and functionalities (or interfaces): under the influence of the object oriented paradigm, the distinction between objects and aspects is all too often forgotten. That’s unfortunate as this difference is congruent with the one between business objects on one hand, business operations on the other hand.
Over generalization: reuse is usually achieved by factoring out useful aspects or factoring off useless ones. In both cases the temptation is to repeat the operation until nothing could be added to the scope. Such “flight for abstraction” will inevitably overtake the proper level of reuse and begets models void of any anchor to business relevancy.
Scalability: while reuse is about separation of concerns and complexity management, those two criteria don’t have to pull in the same direction. When they don’t, variants will be dispersed across artifacts and their processing will suffer a combinatorial explosion if the system has to be scaled up.
Obsolescence: shelf lives of development assets can be defined by each or both business or technical relevancy. Assuming spans either coincide or are managed independently, they should be explicitly taken into account before any reuse.
Those obstacles can be managed providing that models:
Provide some built-in traceability mechanisms between knowledge and artifacts.
Sorting out reuse concerns with differentiated inheritance.
Economics of Reuse and Sustainable Development
Sustainable system development is the ability to meet present business requirements while enhancing system capability to support future ones. Clearly reuse is not the only factor of sustainability, with architectures, returns, and risk management being pivotal. But the economics of reuse encompass most of other factors.
Architectures are clearly first to be considered, as epitomized by MDA:
Reuse of development assets rooted in enterprise architecture is not an option: system functionalities are meant to support business processes as they are (a).
At the other end of the development process, reuse of software designs and components across technical architectures should bring benefits in quality and costs (c).
In between reuse of system functionalities is necessary to guarantee the robustness and continuity of functional architectures; it should also leverage the benefits of reuse of enterprise and development assets (b).
Reuse of models is at the core of the MDA framework
Regarding returns, reuse through generic components, rules engines, or semantic domains can be directly supported by development tools, bypassing explicit models of functional architectures. That makes their costs/benefits analysis both simpler and well circumscribed. That is not the case for system functionalities which stand at the hub of perspectives. As a consequence, costs/benefits should be analyzed as a whole:
Regarding business assets, a clear distinction must be maintained between specific and shared knowledge, reuse being considered for the latter only.
Regarding the reuse of business assets as functional ones, services clearly offer the best returns. Otherwise costs/benefits are to be assessed, from reuse of vocabulary and semantics domains (straightforward, limited overheads), to canonical models and enterprise application integration (contingent, significant overheads).
Economics of reuse will ultimately be set by traceability mechanisms linking enterprise and business knowledge on one hand, components designs on the other hand. Even for services (c), if at a lesser degree, the business case for reuse will be decided by leveraged benefits and non cumulative costs. Hence the importance of maintaining the distinction between identified structures and associated aspects from business (a) and functional (b) requirements, to components interfaces (d) and structures (e).
Maintaining the distinction between structures and aspects from business (a) and functional (b) requirements, to components interfaces (d) and structures (e).
Finally, reuse may also play a significant role in risk management, especially when risks are managed according to their source:
Changes in business contexts can usually occurs along two frequency waves: short, for market opportunities, and long, for an organization’s continuity. Associated risks could be better managed if corresponding knowledge were managed accordingly.
System architectures are meant to evolve in synch with organization continuity; were technological environment or corporate structures subject to unexpected changes, reusable functional assets would be of great help.
Given that enterprise IT can no longer be self-contained and operate in isolation, reusable designs may provide buffers to technological risks and help exploiting unexpected business opportunities.
Reuse of development artifacts can come by design or as an afterthought. While in the latter case artifacts may have been originally devised for specific contexts and purposes, in the former case they would have been originated by shared concerns and designed according architectural constraints and mechanisms.
Reinventing the Wheel ? (Ready-made, M. Duchamp)
Architectures for their part are about stable and sound assets and mechanisms meant to support activities which, by nature, must be adaptable to changing concerns. That is precisely what reusable assets should be looking for, and that may clarify the rationale supporting models and languages:
Why models: to describe shared (i.e reused) artifacts along development processes.
Why non specific languages: to support the sharing of models across business domains and organizational units.
Why model layers: to manage reusable development assets according architectural concerns.
Reuse Perspective: Business Domains vs Development Artifacts
As already noted, software artifacts incorporate contents from two perspectives:
Domain models describe business objects and processes independently of the way they are supported by systems.
Development models describe how to design and implement system components.
Artifacts reflect external as well as development concerns.
As illustrated by agile methods and domain specific languages, that distinction can be ignored when applications are self-contained and projects ownership is shared. In that case reusable assets are managed along business domains, functional architectures are masked, and technical ones are managed by development tools.
Otherwise, reusable assets would be meaningless, even counterproductive, without being associated with clearly defined objectives:
Domain models and business processes are meant to deal with business objectives, for instance, how to assess insurance premiums or compute missile trajectory.
System functionalities lend a hand in solving business problems. Use cases are widely used to describe how systems are to support business processes, and system functionalities are combined to realize use cases.
Platform components provide technical solutions as they achieve targeted functionalities for different users, within distributed locations, under economic constraints on performances and resources.
Context and purpose of reusable assets
Whatever the basis, design or afterthought, reusing an artifact comes as a solution to a specific problem: how to support business requirements, how to specify system functionalities, how to implement system components. Describing problems and solutions along architecture layers should therefore be the backbone of reusable assets management.
Model and Architecture Layers
According model driven architecture principles, models should be organized around three layers depending on contents:
Computation independent models (CIMs) describe business objects and processes independently of the way they are supported by system functionalities. Contents are business specific that can be reused when functional architectures are modified (a). Business specific contents (e.g business rules) can also be reused when changes do not affect functional architectures and may therefore be directly applied to platform specific models (c).
Platform independent models (PIMs) describe system functionalities independently of supporting platforms. They are reused to design new supporting platforms (b).
Platform specific models (PSMs) describe software components. They are used to implement software components to be deployed on platforms (d).
Model and Architecture Layers
Not by chance, invariants within model layers can also be associated with corresponding architectures:
Enterprise architecture (as described by CIMs) deals with objectives, assets and organization associated with the continuity of corporate identity and business capabilities within a regulatory and market environment.
Functional architecture (as described by PIMs) deals with the continuity of systems functionalities and mechanisms supporting business processes.
Technical architecture (as described by PSMs) deals with the feasibility, interoperability, efficiency and economics of systems operations.
That makes architecture invariants the candidates of choice for reusable assets.
Enterprise Architecture Assets
Systems context and purposes are set by enterprise architecture. From an engineering perspective reusable assets (aka knowledge) must include domains, business objects, activities, and processes.
Domains are used to describe the format and semantics of elementary features independently of objects and activities.
Business objects identity and consistency must be maintained along time independently of supporting systems. That’s not the case for features and rules which can be modified or extended.
Activities (and associated roles) describe how business objects are to be processed. Semantics and records have to be maintained along time but details of operations can change.
Business processes and events describe how activities are performed.
Enterprise Architecture Assets (anchors and semantic domains)
As far as enterprise architecture is concerned, structure and semantics of reusable assets should be described independently of system modeling methods.
Structures can be unambiguously described with standard connectors for composition, aggregation and reference, and variants by subsets and power-types, both for static and dynamic partitions.
With regard to business activities, semantics are set by targets:
Processing of physical objects.
Processing of notional objects.
Agents decisions.
Processing of events.
Computations.
Control of processes execution.
Regarding business objects, semantics are set by what is represented:
State of physical objects.
State of notional object.
History of roles.
Events.
Computations.
Execution states.
Enterprise Architecture Assets (with variants and stereotypes)
Enterprise assets are managed according identification, structure, and semantics, as defined along a business perspective. When reused as development artifacts the same attributes will have to be mapped to an engineering perspective.
Use Cases: A bridge between Enterprise and System Architectures
Systems are supposed to support the continuity and consistency of business processes independently of platforms technologies. For that purpose two conditions must be fulfilled:
Identification continuity of business domains: objects identities are kept in sync with their system representations all along their life-cycle, independently of changes in business processes.
Semantic continuity of functional architectures: the history of system representations can be traced back to associated business operations.
Hence, it is first necessary to anchor requirements objects and activities to persistency and functional execution units.
Reusing persistency and functional units to anchor new requirements to enterprise architecture.
Once identities and semantics are properly secured, requirements can be analyzed along standard architecture levels: boundaries (transient objects, local execution), controls (transient objects, shared execution), entities (persistent objects, shared execution).
The main objective at this stage is to identify shared functionalities whose specification should be factored out as candidates for reuse. Three criteria are to be considered:
System boundaries: no reusable assets can stand across systems boundaries. For instance, were billing outsourced the corresponding activity would have to be hid behind a role.
Architecture level: no reusable assets can stand across architecture levels. For instance, the shared operations for staff interface will have to be regrouped at boundary level.
Coupling: no reusable asset can support different synchronization constraint. For instance, checking in and out are bound to external events while room updates and billing are not.
Using stereotypes to identify shared functionalities along architecture levels
It’s worth to note that the objectives of requirements analysis do not depend on the specifics of projects or methods:
Requirements are to be anchored to objects identities and activities semantics either through use cases or directly.
Functionalities are to be consolidated either within new requirements and/or with existing applications.
The Cases for Reuse
As noted above, models and non specific languages are pivotal when new requirements are to be fully or partially supported by existing system functionalities. That may be done by simple reuse of current assets or may call for the consolidation of existing and new artifacts. In any case, reusable assets must be managed along system boundaries, architecture levels, and execution coupling.
For instance, a Clean Room use case goes like: the cleaning staff manages a list of rooms to clean, checks details for status, cleans the room (non supported), and updates lists and room status.
Reuse of Functionalities
Its realization entails different kinds of reuse:
Existing persistency functionality, new business feature: providing a cleaning status is added to the Room entity, Check details can be reused directly (a).
Consolidated control functionality and delegation: a generic list manager could be applied to customers and rooms and used by cleaning and reservation use cases (b).
Specialized boundary functionality: staff interfaces can be composed of a mandatory header with optional panels respectively for check I/O and cleaning (c).
Reuse and Consolidation of functionalities
Reuse and Functional Architecture
Once business requirements taken into account, the problem is how to reuse existing system functionalities to support new functional requirements. Beyond the various approaches and terminologies, there is a broad consensus about the three basic functional levels, usually labelled as model, view, controller (aka MVC):
Model: shared and a life-cycle independent of business processes. The continuity and consistency of business objects representation must be guaranteed independently of the applications using them.
Control: shared with a life-cycle set by a business process. The continuity and consistency of representations is managed independently of the persistency of business objects and interactions with external agents or devices.
View: what is not shared with a life-cycle set by user session. The continuity and consistency of representations is managed locally (interactions with external agents or devices independently of targeted applications.
Service: what is shared with no life-cycle.
The Cases for Functional Reuse
Assuming that functional assets are managed along those levels, reuse can be achieved by domains, delegation, specialization, or generalization:
Semantic domains: shared features (addresses, prices, etc) should reuse descriptions set at business level.
Delegation: part of a new functionality (+) can be supported by an existing one (=).
Specialization: a new functionality is introduced as an extension (+) of an existing one (=).
Generalization: a new functionality is introduced (+) and consolidated with existing ones (~) by factoring out shared features (/).
It must be noted that while reuse by delegation operates at instance level and may directly affect coupling constraints on functional architectures, that’s not the case for specialization and generalization which are set at type level and whose impact can be dealt with by technical architectures.
Single-Responsibility Principle (SRP) : software artifacts should have only one reason to change.
Open-Closed Principle (OCP) : software artifacts should be open for extension, but closed for modification.
Liskov Substitution Principle (LSP): Subtypes must be substitutable for their base types. In other words a given set of instances must be equally mapped to types whatever the level of abstraction.
Dependency-Inversion principle (DIP): high level functionalities should not depend on low level ones. Both should depend on abstract interfaces.
Interface-Segregation Principle (ISP): client software artifacts should not be forced to depend on methods that they do not use.
Reuse by Delegation
Delegation should be considered when different responsibilities are mixed that could be set apart. That will clearly foster more cohesive responsibilities and may also bring about abstract (i.e functional) descriptions of low level (i.e technical) operations.
Reuse by Delegation
Reuse may be actual (the targeted asset is already defined) or forthcoming (the targeted asset has to be created). Service Oriented Architectures are the archetypal realization of reuse by delegation.
Since it operates at instance level, reuse by delegation may overlap functional layers and therefore introduce coupling constraints on data or control flows that could not be supported by targeted architectures.
Reuse by Specialization
Specialization is to be considered when a subset of objects has some additional features. Assuming base functionalities are not affected, specialization fulfills the open-closed principle. And being introduced for a subset of the base population it will also guarantee the Liskov substitution principle.
Reuse by Specialization
Reuse may be actual (a base type already exists) or forthcoming (base and subtype are created simultaneously).
Since it operates at type level, reuse by specialization is supposed to be dealt with by technical architectures. As a corollary, it should not overlap functional layers.
Reuse by Generalization
Generalization should be considered when different sets of objects share a subset of features. Contrary to delegation and specialization, it does affect existing functionalities and may therefore introduce adverse outcomes. While pitfalls may be avoided (or their consequences curbed) for boundary artifacts whose execution is self-contained, that’s more difficult for control and persistency ones, which are meant to support multiple execution within shared address spaces.
When artifacts are used to create transient objects run in self-contained contexts, generalization is straightforward and the factoring out of shared features (a) will clearly further artifacts reuse .
Reuse by generalization put open-closed and interface-segregation principles at risk.
Yet, through its side-effects, generalization may also undermine the design of the whole, for instance:
The open-closed principle may be at risk because when part of a given functionality is factored out, its original semantics are meant to be modified in order to be reused by siblings. That would be the case if authorize() was to be modified for initial screen subtypes as a consequence of reusing the base screen for a new manager screen (b).
Reuse by generalization may also conflict with single-responsibility and interface-segregation principles when a specialized functionality is made to reuse a base one designed for its new siblings. For instance, if the standard reservation screen is adjusted to make room for manager screen it may take into account methods specific to managers (c).
Those problems may be compounded when reuse is applied to control and persistency artifacts: when a generic facility handler and the corresponding record are specialized for a new reservation targeting cars, they both reuse instantiation mechanisms and methods supporting multiple execution within shared address spaces; that is not the case for generalization as the new roots for facility handler and reservation cannot be achieved without modifying existing handler and recording of room reservations.
Reuse by Abstraction: Specialization is safer than Generalization
Since reuse through abstraction is based on inheritance mechanisms, that’s where the cases for reuse are to be examined.
Reuse by Inheritance
As noted above, reuse by generalization may undermine the design of boundaries, control, and persistency artifacts. While risks for boundaries are by nature local and limited to static descriptions, at control and persistency layers they affect instantiation mechanisms and shared execution at system level. And those those pitfalls can be circumscribed by a distinction between objects and aspects.
Object types describe set of identified instances. In that case reuse by generalization means that objects targeted by new artifact must be identified and structured according the base descriptions whose reuse is under consideration. From a programming perspective object types will be eventually implemented as concrete classes.
Aspect types describe behaviors or functionalities independently of the objects supporting them. Reuse of aspects can be understood as inheritance or composition. From a programming perspective they will be eventually implemented as interfaces or abstract classes.
Unfettered by programming languages constraints, generalization can be given consistent and unambiguous semantics. As a consequence, reuse by generalization can be introduced selectively to structures and aspects, with single inheritance for the former, multiple for the latter.
Not by chance, that distinction can be directly mapped to the taxonomy of design patterns proposed by the Gang of Four:
Creational designs deal with the instanciation of objects.
Structural designs deal with the building of structures.
Behavioral designs deal with the functionalities supported by objects.
Applied to boundary artifacts, the distinction broadly coincides with the one between main windows (e.g Java Frames) on one hand, other graphical user interface components on the other hand, with the former identifying users sessions. For example, screens will be composed of a common header and specialized with components for managers and staffs. Support for reservation or cleaning activities will be achieved by inheriting corresponding aspects.
Reuse of boundary artifacts through structures and aspects inheritance
Freed from single inheritance constraints, the granularity of functionalities can be set independently of structures. Combined with selective inheritance, that will directly benefit open-closed, single-responsibility and interface-segregation principles.
The distinction between identifying structures on one hand, aspects on the other hand, is still more critical for artifacts supporting control functionalities as they must guarantee multiple execution within shared address spaces. In other words reuse of control artifacts should first and foremost be about managing identities and conflicting behaviors. And that can be best achieved when instantiation, structures, and aspects are designed independently:
Whatever the targeted facility, a session must be created for, and identified by, each user request (#). Yet, since reservations cannot be processed independently, they must be managed under a single control (aka authority) within a single address space.
That’s not the case for the consultation of details which can therefore be supported by artifacts whose identification is not bound to sessions.
Reuse of control artifacts through structures and aspects inheritance
Extensions, e.g for flights, will reuse creation and identification mechanisms along strong (binding) inheritance links; generalization will be safer as it will focus on clearly defined operations. Reuse of aspects will be managed separately along weak (non binding) inheritance links.
Reuse of control artifacts through selective inheritance may be especially useful with regard to dependency-inversion principle as it will facilitate the distinction between policy, mechanism, and utility layers.
Regarding artifacts supporting persistency, the main challenge is about domains consistency, best addressed by the Liskov substitution principle. According to that principle, a given set of instances should be equivalently represented independently of the level of abstraction. For example, the same instances of facilities should be represented identically as such or according their types. Clearly that will not be possible with overlapping subsets as the number of instances will differ depending on the level of abstraction.
But taxonomies being business driven, they usually overlap when the same objects are targeted by different business domains, as could be the case if reservations were targeting transport and lodging services while facility providers were managing actual resources with overlapping services. With selective inheritance it will be possible to reuse aspects without contradicting the substitution principle.
Reuse of persistency artifacts through structures and aspects inheritance
Reuse across Functional Architecture Layers
Contrary to reuse by delegation, which relates to instances, reuse by abstraction relates to types and should not be applied across functional architecture layers lest it would break the separation of concerns. Hence the importance of the distinction between reuse of structures, which may impact on identification, and the reuse of aspects, which doesn’t.
Given that reuse of development artifacts is to be governed along architecture levels (enterprise, system functionalities, platform technologies) on one hand, and functional layers (boundaries, controls, persistency) on the other hand, some principles must be set regarding eligible mechanisms.
Two mechanisms are available for type reuse across architecture levels:
Semantics domains are defined by enterprise architecture and can be directly reused by functionalities.
Design patterns enable the transformation of functional assets into technical ones.
Otherwise reuse policies must follow functional layers:
Base entities are first anchored to business objects (1), with possible subsequent specialization (1b). Generalization must distinguish between structures and aspects lest to break continuity and consistency of representations.
Base controls are anchored to business activities and may reuse entities (2). They may be specialized (2b). Generalization must distinguish between structures and aspects lest to break continuity and consistency of business processes.
Base boundaries are anchored to roles and may reuse controls (3). They may be specialized (3b). Generalization must distinguish between structures and aspects lest to break continuity and consistency of sessions.
Contrary to some unfortunate misconceptions, measurements are as much about collaboration and trust as they are about rewards or sanctions. By shedding light on objectives and pitfalls they prevent prejudiced assumptions and defensive behaviors; by setting explicit quality and acceptance criteria they help to align respective expectations and commitments.
How to put requirements into equations (Lawrence Weiner)
Since projects begin with requirements, decisions about targeted functionalities and resources commitments are necessarily based upon estimations made at inception. Yet at such an early stage very little is known about the size and complexity of the components to be developed. Nonetheless, quality planning all along the engineered process is to be seriously undermined if not grounded in requirements as expressed by stakeholders and users.
Business vs Functional Complexity
Contracts without transparency are worthless, and the first objective is therefore to track down complexity across enterprise architecture and business processes. For that purpose one should distinguish between business and application domains, business processes, and use cases, which describe how system functionalities support business processes.
Processes are defined on domains, system functionalities are set by use cases
Based upon intrinsic metrics computed at domain level, functional metrics are introduced to measure system functionalities supporting business processes and compare alternatives solutions. Finally requirements metrics should also provide a yardstick to assess development models.
Business Requirements Metrics
The first step is to assess the intrinsic size and complexity of business domains and processes independently of system functionalities, and that can be done according symbolic representations:
The footprint includes artifacts for symbolic objects and activities, partitions (objects classifications or activities variants). Symbolic objects and activities are qualified as primary or secondary depending on their identification. The reliability of the footprint is weighted by the explicit qualification of artifacts as primary or secondary.
Symbolic representations for objects and activities are associated with features (attributes or operations) defined within semantic domains.
A part from absolute measurements a number of basic ratios can be computed for anchors (primary objects and activities) and associated partitions.
A robust appraisal of the completeness and maturity of requirements can be derived from:
Percentage of artifacts with undefined identification mechanism.
Percentage of partitions with undefined characteristics (exclusivity, changeability).
The organization and structure of requirements can be estimated by:
Average number of artifacts and partitions by domain (a).
Total number of secondary objects and activities relative to primary ones.
Average and maximum depth of secondary identification.
Total number of primary activities relative to primary objects
Total number of features (attributes and operations) relative to number of artifacts.
Ratio of local features (defined at artifact level) relative to shared (defined at domain level) ones.
Finally intrinsic complexity can be objectively assessed using partitions:
Total number of activity variants relative to object classifications.
Total number of exclusive partitions relative to primary artifacts, respectively for objects and activities.
Percentage of activity variants combined with object classifications.
Average and maximum depth of cross partitions.
It must be noted that whereas those ratios do not depend of any modeling method, they can nonetheless be used to assess requirements or refactor them according specific methods, patterns, or practices.
Functional Requirements Metrics
Functional metrics target the support systems are meant to bring to business processes:
The footprint of supporting functionalities is marked out by roles to be supported, active physical objects to be interfaced, events to be managed, and processes to be executed. As for symbolic representations, corresponding artifacts are to be qualified as primary or secondary depending on their identification, with accuracy and reliability of metrics weighted by the completeness of qualifications.
Functional artifacts (objects, processes, events, and roles) are associated with anchors and features (attributes or operations) defined by business requirements.
From business to functional requirements metrics
Given that use cases are meant to focus on interactions between systems and contexts, they should provide the best basis for functional metrics:
Interactions with users, identified by primary roles and weighted by activities and flows (a).
Access to business (aka persistent) objects, weighted by complexity and features (b).
Control of execution, weighted by variants and couplings (c).
Processing of objects, weighted by variants and features (d).
Processing of actual (analog) events, weighted by features (e).
Processing of physical objects, weighted by features (f).
Additional adjustments are to be considered for distributed locations and synchronous execution.
Use Cases metrics can also provide a basis for quality checks:
Average and maximum number of root use cases relative to business and application domains.
Average and maximum number of root use cases relative to primary activities.
Average and maximum number of secondary use cases relative to root ones.
Average number of primary (for identified) and secondary roles relative to root use cases.
Average number primary (aka external) and secondary (issued by use cases) events relative to root use cases.
Average and maximum number of primary objects relative to use cases.
The number of variants supported by a use case relative to the total associated with its footprint, respectively for persistent and transient objects.
Requirements Metrics, Contracts, and Acceptance Criteria
As noted above, requirements metrics are a means to a dual end, namely to set a price on developments and define corresponding acceptance criteria. Moreover, as far are business is concerned, change is the rule of the game. Hence, if many projects can be set on detailed and stable requirements, projects with overlapping concerns and outlying horizons will usually entail changing contexts and priorities. While the former can be contracted out for fixed prices, the latter should clearly allow some room for manoeuvre, and requirements metrics can help to manage that room.
Assuming that projects are initiated from clearly identified business domains or processes, requirements capture should be set against a preliminary wire-frame referencing new or existing pivotal elements of business (1) and system (2) contexts. Those wire-frames (aka anchors, aka backbones) will be used both for circumscribing envelops and managing changes.
Envelops should be defined along architecture layers: enterprise for business objectives (1), functional for supporting systems (2), and technical for deployment platforms (3). That will set budgets outer limits for given architectural contexts.
Moves within rooms (4) are governed by functional requirements and anchored to pivotal business objects or processed (wire-frame’s nodes) . Their estimations should take into account analogies with previous developments.
Acceptance criteria could then be defined both for invariants (changes are not to overstep architectural constraints) and increments (added functionalities are properly implemented).
Pricing the loops
Requirements Metrics and Agile
While agile development models are meant to be driven by business value, project assessment essentially relies on informed guesses about users stories. That let two questions unanswered:
Given that the business value of a story is often defined at process level, how to align it with its local assessment by project team.
If problem spaces and solution paths are to be explored iteratively, how to reassess dynamically the stories in backlog.
Answers to both questions are to be helped if requirements are qualified with regard to their nature and footprint.
Informed decision making about what to consider and when clearly depend on the nature of stakes. Hence the importance of a reasoned requirements taxonomy setting apart business requirements, system functionalities, quality of service, and technical constraints on platform implementations.
Dynamic assessment and ranking of backlog elements require some hierarchy and modularity:
Architectural options, functional or technical, must be weighted by supported business features and quality of services.
Dynamic ranking of users’ stories implies that their value can be mapped to features metrics at the corresponding level of granularity.
Requirements should be assessed with regard to their nature and their footprint in functional architecture.
Requirements Metrics and Quality Planning
Finally, requirements metrics may also be used to design quality checks to downstream models. With metrics for intrinsic characteristics of business domains on one hand, system functionalities on the other hand, subsequent models can be assessed for consistency, and alternatives solutions compared.
The number of root packages models is to be compared to symbolic containers for business and application domains, and the sub packages to primary (#) objects or activities.
The number of classes in models and packages is to be
The number of actors is to be set against the number of roles, with primary (#) roles standing for identified agents.
The number of root use cases is supposed to coincide with primary (#) activities and roles.
The number of structural inheritance hierarchies should be compatible with the number of frozen and exclusive partitions respectively for objects and activities.
Models are representations and as such they are necessarily set in perspective and marked out by concerns.
Model, Perspective, Concern (R. Doisneau).
Depending on perspective, models will encompass whole contexts (symbolic, mechanic, and human components), information systems (functional components), software (components implementation).
Depending on concerns models will take into account responsibilities (enterprise architecture), functionalities (functional architecture), and operations (technical architecture).
While it may be a sensible aim, perspectives and concerns are not necessarily congruent as responsibilities or functionalities may cross perspectives (e.g support units), and perspectives may mix concerns (e.g legacies and migrations). That conundrum may be resolved by a clear distinction between descriptive and prescriptive models, the former dealing with the problem at hand, the latter with the corresponding solutions, respectively for business, system functionalities, and system implementation.
Models as Knowledge
Assuming that systems are built to manage symbolic representations of business domains and operations, models are best understood as knowledge, as defined by the pivotal article of Davis, Shrobe, and Szolovits:
Surrogate: models provide the description of symbolic objects standing as counterparts of managed business objects and activities.
Ontological commitments: models include statements about the categories of things that may exist in the domain under consideration.
Fragmentary theory of intelligent reasoning: models include statements of what the things can do or can be done with.
Medium for efficient computation: making models understandable by computers is a necessary step for any learning curve.
Medium for human expression: models are meant to improve the communication between specific domain experts on one hand, generic knowledge managers on the other hand.
Surrogates without Ontological Commitment
What You Think Is What You Get
Whereas conventional engineering has to deal with physical artifacts, software engineering has only symbolic ones to consider. As a consequence, design models can be processed into products without any physical impediments: “What You Think Is What You Get.”
Products and Usage are two different things
Yet even well designed products are not necessarily used as expected, especially if organizational and business contexts have changed since requirements capture.
Models and Architectures
Models are partial or complete descriptions of existing or intended systems. Given that systems will eventually be implemented by software components, models and programs may overlap or even be congruent in case of systems made exclusively of software components. Moreover, legacy systems are likely to get along together with models and software components. Such cohabitation calls for some common roof, supported by shared architectures:
Enterprise architecture deals with the continuity of business concerns.
System architecture deals with the continuity of systems functionalities.
Technical architecture deals with the continuity of systems implementations.
That distinction can also be applied to engineering problems and solutions: business (>enterprise), organization (supporting systems), and development (implementations).
Problems and solutions must be set along architecture layers
On that basis the aim of analysis is to define the relationship between business processes and supporting systems, and the aim of design is to do the same between system functionalities and components implementation.
Whatever the terminology (layers or levels), what is at stake is the alignment of two basic scales:
Architectures: enterprise (concepts), systems (functionalities), and platforms (technologies).
If systems could be developed along a “fire and forget” procedure models would be used only once. Since that’s not usually the case bridges between business contexts and supporting systems cannot be burned; models must be built and maintained both for business and system architectures, and the semantics of modeling languages defined accordingly.
Languages, Concerns, Perspectives
Apart for trivial or standalone applications, engineering processes will involve several parties whose collaboration along time will call for sound languages. Programming languages are meant to be executed by symbolic devices, business languages (e.g B.P.M.) are meant to describe business processes, and modeling languages (e.g UML) stand somewhere in-between.
As far as system engineering is concerned, modeling languages have two main purposes: (1) describe what is expected from the system under consideration, and (2) specify how it should be built. Clearly, the former belongs to the business perspective and must be expressed with its specific words, while the latter can use some “unified” language common to system designers.
The Unified Modeling Language (UML) is the outcome of the collaboration between James Rumbaugh with his Object-modeling technique (OMT), Grady Booch, with his eponymous method, and Ivar Jacobson, creator of the object-oriented software engineering (OOSE) method.
Whereas UML has been accepted as the primary standard since 1995, it’s scope remains limited and its use shallow. Moreover, when UML is effectively used, it is often for the implementation of Domain Specific Languages based upon its stereotype and profile extensions. Given the broadly recognized merits of core UML constructs, and the lack of alternative solutions, such a scant diffusion cannot be fully or even readily explained by subordinate factors. A more likely pivotal factor may be the way UML is used, in particular in the confusion between perspectives and concerns.
Perspectives and Concerns: business, functionalities, implementation
Languages are useless without pragmatics which, for modeling ones means some methodology defining what is to be modeled, how, by who, and when. Like pragmatics, methods are diverse, each bringing its own priorities and background, be it modeling concepts (e.g OOA/D), procedures (e.g RUP), or collaboration agile principles (e.g Scrum). As it happens, none deals explicitly with the pivotal challenges of the modeling process, namely: perspective (what is modeled), and concern (whose purpose).
Processes can be designed, assessed and improved by matching development patterns with development strategies.
Matching Concerns and Perspectives
As famously explained by Douglas Hofstadter’s Eternal Golden Braid, models cannot be proven true, only to be consistent or disproved.
Depending on language, internal consistency can be checked through reviews (natural language) or using automated tools (formal languages).
Refutation for its part entails checks on external consistency, in other words matching models and concerns across perspectives. For that purpose modeling stations must target well defined sets of identified objects or phenomena and use clear and non ambiguous semantics. A simplified (yet versatile), modeling cycle could therefore be exemplified as follows:
Identify a milestone relative to perspective, concern, and architecture.
Select anchors (objects or activities).
Add connectors and features.
Check model for internal consistency.
Check model for external consistency, e.g refutation by counter examples.
