Spaces, Paths, Paces (Part 2)

Objective

As previously noted, embarking for sizable and lengthy project on the assumption that detailed scope and schedules can be set upfront is a very hazardous policy. Alternatively, agile development models carry out specifications, development, and quality assurance into integrated iterations, making room for a progressive exploration of problem spaces and solution paths, and consequently for informed decision-making and better risk management.

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Paths & Paces (S.Dali)

Yet, whatever the method, at some point, scope and features will have to be committed; and with targeted features set dynamically, planned schedules are no more an option. Hence the need to reconsider the way time is taken into account.

Dependencies: Playing for Time

Paraphrasing Einstein, one may say that the only reason for processes is so that everything doesn’t happen at once. Why is that ?

First, processes are meant to support informed decisions. If problem spaces and solution paths cannot be settled upfront they must be done progressively. And that will clearly introduce informational dependencies supporting:

  • Decisions about what is to be done: alternative paths and their priorities
  • Decisions about how it should be done: supported features and their priorities
  • Decisions about how it can be done: quality assurance and acceptance.

In that context the objective of development processes is to do as much of definition, building, and acceptance, and to delay decisions in order to gather the most of the relevant information until the “last responsible moment”, i.e without preempting any of the initial set of alternative options.

Assuming informed decisions are supported by architecture knowledge, processes must take into account engineering constraints regarding:

  • Technical dependencies associated to the nature of development flows and environments.
  • Functional dependencies associated to products functionalities and supporting systems.
  • Organizational dependencies associated to business units and localization.
Iterating across architecture layers
Iterating across architecture layers

While those dependencies are defined within architecture layers, their impact on the organization of work units may have to be consolidated when projects are carried out across layers.

Schedules: Running for Time

Whether development cycles stay within or run across architecture layers, there will be no way to decide about deliveries and schedules at project inception. In other words dependencies will have to be sorted out and planning settled along the road.

Project planning means consolidating overlapping time-frames. That may not be a problem for projects set within single architecture layers (e.g migration), but that should definitively be taken into account when heterogeneous time-frames govern changes across architecture layers. With regard to changes managed at project level (endogenous events), the consolidation may be done within project time-frame; but that will not be possible for changes occurring in non managed environments (exogenous events).

Those events are set in time-frames governed by their own rationale (e.g business, organization, or technology) that cannot be subsumed into the engineering timetable:

  • At enterprise level, time is set by business context. Both business objectives and business process solutions are meant to be decided with regard to actual (aka exogenous) business opportunities (a).
  • At system level, time is set by project planning (endogenous events). Given functional requirements (e.g users’ stories or use cases), architects and designers have to decide about the scheduling of systems functionalities and services, and the release of corresponding applications. Since those decisions are not directly exposed to exogenous events, they can be made according to engineering constraints and resources availability (b).
  • At platform level, time is set by business and operational objectives (endogenous events), and technical contexts (exogenous events). Assuming that risks are evenly set, the problem is to align endogenous (managed by project) with exogenous (anticipated from contexts) events: too early releases may preclude later but more useful ones; too late releases may hamper operations (c).
The texture of time differs across architecture layers (yellow for endogenous, red for exogenous)
The texture of time differs across architecture layers (yellow for endogenous, red for exogenous)

Whereas those time-scales are to be synchronized, there is no reason they would be congruent. Hence the need of some mechanism, static (e.g milestones) or dynamic (e.g backlogs) supporting the scheduling of work units.

From Milestones to Backlogs

As considered elsewhere, phased models of development may offer some benefits when dependencies originate from different environments or involve different authorities; yet they may impose still bigger penalties when requirements cannot be fully settled upfront. Conversely, agile approaches are the option of choice when complex problem spaces and solution paths are to be explored and refined progressively; but may prove ineffective in dealing with business, organizational, or technical dependencies set from outside projects. That difference of perspective is reflected by the mechanisms used to manage dependencies: milestones or backlogs.

Sorting out dependencies means consolidating informational and engineering constraints across architecture layers. When problem spaces and solution paths cannot be managed under the same authority (heterogeneous dependencies) phased development models use milestones to consolidate expectations and commitments across layers and time-frames.

Otherwise (homogeneous dependencies) agile development models use backlogs to explore problem spaces and solution paths, whatever the architecture layer and nature of dependencies. With the benefit of shared ownership, business events are propagated all along development paths, governing backlogs of stories (business requirements), features (engineering constraints), and releases (operational requirements).

Development processes must consolidate organizational, functional, and technical dependencies.
Backlogs can be used to consolidate organizational, functional, and technical dependencies.

Along that perspective  backlogs and milestones can be seen as mirrored solutions of the same problem, namely how to deal with the functional and timing dimension of dependencies: backlogs deal only with the functional dimension as they consider the dependencies between tasks without taking into account their actual timing; milestones look from the opposite direction, anchoring aggregate tasks to timetables before considering dependencies between items. Depending on the point of view:

  • Backlogs may appear as stacks of stones: the milestones are fragmented and the initial sequence translated into queues.
  • Milestones may appear as flattened backlogs: dependencies are frozen and stories are ironed out before being merged into heaps.
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Milestones can be seen as “flattened” backlogs, or, alternatively, backlogs as stacked milestones

Hence, with regard to the ranking of dependencies, the difference between backlogs and milestones is of granularity, finer for the former, coarser for the latter. But with regard to execution, the difference is of nature: contrary to milestones, backlogs don’t fix any timing.

That distinction between sequence (functional ranking) and schedule (time ranking) may be critical when iterative development has to be combined with heterogeneous dependencies.

Collaboration Levels

As noted above, the planning of work units must take into account three criteria:

  • The dependencies between tasks, engineering or informational, determine the sequencing independently of actual timing. They are derived from technical or organizational constraints.
  • The schedules anchor the start and completion of tasks to time-frames. Some time-frames are set within the enterprise (e.g resources availability), others are set from outside (e.g regulations or business opportunities).
  • Pace determine the elapsed time taken to complete a task. It may be seen as a metronome used to adjust the throughput depending on resources availability on one hand, quality requirements on the other hand.
Milestones and Backlogs Capabilities
Milestones and Backlogs Capabilities

At first sight, comparison shows backlogs to outdo milestones on all three accounts: dependencies are managed at finer granularity, which enables dynamic scheduling and releases management; last but not least, due to iteration cycles and time-boxing, development paces can be aligned with resources and quality requirements. Yet, that edge can be misleading if dependencies translate into unmanageable or multiple backlogs.

Collaboration is at the core of iteration cycles, and it can only be achieved through shared and dynamic management of backlogs. Assuming a set of stories, each new cycle has to be preceded by decisions regarding priorities, refinements, and responsibilities; and if informational dependencies have to be taken into account, these decisions must be taken collectively and directly:

  • Collectively: decisions about priorities and refinements must be negotiated between team members from both sides of the business/system (or users/developers) divide.
  • Directly: all decisions must be taken by the team itself, without any organizational mediation or external interference.

But those conditions could be thwarted if different projects have to develop stories with shared features implemented on different platforms.

Shared features and cross implementations
Shared features and cross implementations could thwart collective and direct decision-making.

In that case consolidation mechanisms could bring back fixed scope/schedule configurations:

  • Different teams, each with its own specific users and developers concerns, will have to commit to agreed features and timetables.
  • By involving independent organizational units, those decisions will entail some procedural mediation carried out independently of iteration cycles.

Yet, that shouldn’t necessarily be the end of the stories, providing some mechanism could be found to synchronize iteration cycles. And that could be achieved with blackboards.

From Backlogs to Blackboards

Blackboards can be understood as shared backlogs stripped from their ranking mechanism so that items can be consulted and dealt with by different project teams. Alternatively, they can also be seen as timetables stripped from scheduling mechanism. Either way, those views illustrate how blackboards may support shared dependencies without forcing them into time-frames:

  • Shared features are posted on a single blackboard where their status can be consulted and updated by the teams in charge of the stories concerned.
  • Development teams post their releases on blackboards according to targeted platforms.
Blackboards are used to manage shared dependencies
Blackboards are used to manage shared dependencies and differentiated stories’ contents

As it happens, that approach also answers the recurring question of stories’ nature and granularity: with the benefit of blackboards, fine-grained stories can be indexed as business cases, use cases, or new releases and cohabit in backlogs.

Combining backlogs with blackboards provides a collaboration mechanisms supporting external dependencies without impairing teams ownership of iteration cycles. Yet it is not a substitute for schedules as it doesn’t deal with the alignment of cycles with enterprise time-frames.

Time boxes as Pacemakers

Milestones and backlogs are synchronization mechanisms. The former, combined with timetables, coordinate teams along time-spans;  the latter, combined with time boxes, coordinate tasks between cycles. Hence, while both aim at the same objective, namely that everything doesn’t happen at once, their understanding of time is very different: timetables are bound to an external measure of time, time boxes are just arbitrarily fixed intervals that can be tethered to any time-frame.

As a consequence, time boxes can be applied at different levels. At system level they are associated to project backlogs and used to set the tempo of iteration cycles. At enterprise level they are associated to business objectives and anchored to strategic plans. Assuming those plans are described by epics, it would be possible to use different tempos depending on level (enterprise or systems) or even applications.

Time boxes can be combined with blackboards to synchronize tempos between enterprise level (T) and interrelated projects (T/x and T/y).
Time boxes can be combined with blackboards to synchronize tempos between enterprise level (T) and interrelated projects (T/x and T/y).

Ideally, differentiated tempos should foster an emerging harmony between melody (users’ stories) and accompaniment (supporting systems) converging into the fulfillment of strategic objectives. Practically, the alignment of releases with epics cannot be taken for granted.

Squaring the Circles: Project Planning

Agile approaches are based upon the dynamic exploration of problem spaces and the iterative development of solution paths, the objective being to maximize the value of functional requirements under the constraints of technical ones. Yet, with spaces and paths defined dynamically, standard exploration procedures like breath or depth-first traversal are useless because the ranking of paths is part of the solution, which means that priorities must be revised at the end of each cycle.

Nonetheless, explorations cannot be everlasting and there must be some end to the story, when stakeholders get what they expect (or accept what they get) and users can begin to reap the benefits. Fixed scope and schedules being ruled out, the problem is to align iterations outcomes (b) with business objectives (a). Solutions can be positioned between two archetypal approaches, one driven by business objectives, the other by development tasks.

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How to align tasks (b) with objectives (a)

The first approach would see development teams take commitments with regard to broadly defined objectives: with problem spaces represented by trees progressively refined and explored, commitments can be made collectively on sets of solution paths within still undefined sub-trees (b1). The team will then take responsibility for the details of iteration cycles and will adjust its throughput as to align releases with business objectives.

Alternatively, and providing a finer granularity can be obtained and managed, stories could be broken down into tasks for which commitments will be made individually by team members (b2). Assuming that the tasks workloads can be assessed, iterations could then be planned and releases scheduled on the basis of time boxes parameters.

Commitments can be made collectively with regard to objectives (b1) or individually with regard to stories (b2).
Commitments can be made collectively with regard to objectives (b1) or individually with regard to tasks (b2).

Not surprisingly, the choice of a planning policy is to be conditioned by the granularity of work units:

  • Task based planning requires finer grained stories and comes with a phased flavor by reintroducing analysis. That may stretch the intervals between iterations and increase management overheads.
  • Objective based planning allows for coarser grained stories and is more in line with agile spirit. Yet that may increase the length of iterations and affect their transparency.

All things considered, some feedback loop may be needed when deciding on the size of stories because shorter ones do not necessarily decrease the time to market as they may generate bigger backlogs and exponential overheads in case of complex dependencies.

Balancing Pushes and Pulls: Lean and Just-in-Time Workflows

When push comes to shove project planning turns into conflict management. That may happen with phased development models as well as with agile ones. With the former that will be due to applications forcibly pulled out whatever their value for users and reliability in order to meet unrealistic expectations. With the latter that will be due to requirements forcibly pushed into backlogs disregarding their size and exponential complexity.

The way out of this dilemma is a feedback mechanism between project teams pushing releases and business stakeholders pulling applications. And that is the rationale behind the Kanban development model:

  • Visualize workflow: up-to-date expectations, constraints, commitments and achievements must be clearly and selectively visible to all concerned. that can be done with backlogs and blackboards.
  • Limit work in progress: that can be obtained by regulating the selection of solution paths through limits on the size of backlogs and the fragmentation of users’ stories according to their architecture footprint.
  • Measure and manage flows: metrics and process design can be significantly improved if flows are differentiated depending on architecture layer (business, systems, platforms).
  • Make process policies explicit: that is already a cornerstone of agile backlog management; those principles should also apply to backboards.
  • Use models to recognize improvement opportunities: while initially ignored, the benefits of models in agile development is progressively acknowledged. Those benefits have be illustrated in the first part of this article by the use of activity diagrams for the exploration of problems spaces and solutions paths.

Applying those principles will bring about lean processes and just-in-time workflows, improving both users’ value and software quality.

Further Reading

External Links

From Stories to Models

Objective

Assuming, for the sake of the argument, that programs are models of implementations, one may also argue that the main challenge of software engineering is to translate requirements into models. But, contrary to programs, nothing can be assumed about requirements apart from being stories told by whoever will need system support for his business process.

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Telling Stories with Models

Along that reasoning, one may consider the capture and analysis of requirements under the light of two archetypal motifs of storytelling, the Tower of Babel and the Rashomon effect:

  • While stakeholders and users may express their requirements using their own dialects, supporting applications will have to be developed under the same roof. Hence the need of some lingua franca to communicate with their builders.
  • A shared language doesn’t necessary mean common understandings; as requirements usually reflect local and time dependent business opportunities and goals, they may relate to different, if not conflicting, aspects of contexts and concerns that will have to be consolidated, eventually.

From such viewpoints, the alignment of system models to business stories clearly depends on languages and narratives discrepancies.

Business to System Analyst: Your language or mine ?

Stories must be told before being written into models, and that distinction coincides with the one between spoken and written languages or, on a broader perspective,  between direct (aka performed) and documented communication.

Direct communication (by voice, signs, or mime) is set by time and location and must convey contexts and concerns instantly; that’s what happens when requirements are first expressed by business analysts with regard to actual and specific goals.

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Direct communication requires instant understanding

Written languages and documented communication introduces a mediation, enabling stories to be detached from their native here and now; that’s what happens with requirements when managed independently of their original contexts and concerns.

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Documented communication makes room for mediation

The mediation introduced by documented requirements can support two different objectives:

  1. Elicitation: while direct communication calls for instant understanding through a common language, spoken or otherwise, written communication makes room for translation and clarification. As illustrated by Kanji characters, a single written language can support different spoken ones; that would open a communication channel between business and system analysts.
  2. Analysis: since understanding doesn’t mean agreement, mediation is often necessary in order to conciliate, arbitrate or consolidate requirements; for that purpose symbolic representations have to be introduced.

Depending on (1) the languages used to tell the stories and (2) the gamut of concerns behind them, the path from stories to models may be covered in a single step or will have to mark the two steps.

Context and Characters

Direct communication is rooted in actual contexts and points to identified agents, objects or phenomena. Telling a story will therefore begin by introducing characters and objects supposed to retain their identity all along; characters will also be imparted with behavioral capabilities and the concerns supposed to guide them.

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Stories start with characters and concerns

With regard to business, stories should therefore be introduced by a role, an activity, and a goal.

  • Every story is supposed be told from a specific point of view within the organization. That should be materialized by a leading role; and even if other participants are involved, the narrative should reflect this leading view.
  • If a story is to provide a one-lane bridge between past and future business practices, it must focus on a single activity whose contents can be initially overlooked.
  • Goals are meant to set specific stories within a broader enterprise perspective.

After being anchored to roles and goals, activities will have to be set within boundaries.

Casings and Splits

Once introduced between roles (Who) and goals (Why), activities must be circumscribed with regard to objects (What), actions (How), places (Where) and timing (When). For that purpose the best approach is to use Aristotle’s three unities for drama:

  1. Unity of action: story units must have one main thread of action introduced at the beginning. Subplots, if any, must return to the main plot after completion.
  2. Unity of place: story units must be located into a single physical space where all activities can be carried out without depending on the outcome of activities performed elsewhere.
  3. Unity of time: story units must be governed by a single clock under which all happenings can be organized sequentially.

Stories, especially when expressed vocally, should remain short and, if they have to be divided, splits should not cross units boundaries:

  • Action: splits are made to coincide with variants set by agents’ decisions or business rules.
  • Place: splits are made to coincide with variants in physical contexts.
  • Time: splits are made to coincide with variants in execution constraints.

When stories refer to systems, those constraints should become more specific and coincide with interaction units triggered by a single event from a leading actor.

Filling the blanks

If business contexts, objectives, and roles can be identified with straightforward semantics set at corporate level, meanings become more complex when stories are to be fleshed out with details defined by the different business units. That difficulty can be managed through iterative development that will add specifics to stories within the casing invariants:

  • Each story is developed within a single iteration whose invariants are defined by its action, place, and time-scale.
  • Development proceed by increments whose semantics are defined within the scope set by invariants: operations relative to activities, features relative to objects, events relative to time-scales.

A story is fully documented (i.e an iteration is completed) when no more details can be added without breaking the three units rule or affecting its characters (role and goal) or the semantics of features (attributes and operations).

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Iterations: a story is fully fleshed out when nothing can be changed without affecting characters’ features or their semantics.

From Documented Stories to Requirements

Stories must be written down before becoming requirements, further documented by text, model, or code:

  • Text-based documentation uses natural language, usually with hypertext extensions. When analysts are not familiar with modeling languages it is the default option for elicitation and the delivery of comprehensive, unambiguous and consistent requirements.
  • Models use dedicated languages targeting domains (specific) or systems (generic). They are a necessary option when requirements from different sources are to be consolidated before being developed into code.
  • Code (aka execution model) use dedicated languages targeting execution environments. It is the option of choice when requirements are self-contained (i.e not contingent to external dependencies) and expressed with formal languages supporting automated translation.

Whatever their form (user stories, use cases, hypertext, etc), documented requirements must come out as a list of detached items with clearly defined dependencies. Depending on dependencies, requirements can be directly translated into design (or implementation) models or will have to be first consolidated into analysis models.

Telling Models from Stories

Putting aside deployment, development models can be regrouped in two categories:

  • Analysis models describe problems under scrutiny, the objective being to extract relevant aspects.
  • Design models (including programs) describe solutions artifacts.
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Descriptions and specifications look from different perspectives

Seen from the perspective of requirements, the objective of models is therefore to organize the contents of business stories into relevant and useful information, in other words software engineering knowledge.

Following the principles set by Davis, Shrobe, and Szolovits for Knowledge Management (cf readings), such models should meet two groups of criteria, one with regard to communication, the other with regard to symbolic representation.

As already noted, models are introduced to support communication across organizational structures or intervals of time. That includes communication between business and systems analysts as well as development tools. Those aspects are supposed to be supported by development environments.

As for model contents, the ultimate objective is to describe the symbolic representations of the business objects and processes targeted by requirements:

  • Surrogates: models must describe the symbolic counterparts of actual objects, events and relationships.
  • Ontological commitments: models must provide sets of statements about the categories of things that may exist in the domain under consideration.
  • Fragmentary theory of intelligent reasoning: models must define what artifacts can do or can be done with.

The main challenge of analysis is therefore to map the space between requirements (concrete stories) and models (symbolic representations), and for that purpose traditional storytelling may offer some useful cues.

From Fictions to Functions

Just like storytellers use cliches and figures of speech to attach symbolic meanings to stories, analysts may use patterns to anchor business stories to systems models.

Cliches are mental constructs with meanings set in collective memory. With regard to requirements, the equivalent would be to anchor activities to primitives operations (e.g CRUD), and roles to functional stereotypes.

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Archetypes can be used to anchor stories to shared understandings

While the role of cliches is to introduce basic items, figures of speech are used to extend and enrich their meanings through analogy or metonymy:

  • Analogy is used to identify features or behaviors shared by different stories. That will help to consolidate the description of business objects and activities and points to generalizations.
  • Metonymy is applied when meanings are set by context. That points to aggregate or composite objects or activities.

Primitives, stereotypes, generalization and composition can be employed to map requirements to functional patterns. Those will provide the building blocks of models and help to bridge the gap between business processes and system functionalities.

Further Reading

External Readings

On Pies & Skies: Abstraction in Models

Objective

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.

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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.

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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.

Models & Semantics

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 analyst figure maps from territories, software architects create territories from maps
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:

  1. Awareness of contexts: mind the business pie, drop everything else.
  2. Domains of concern: say what features mean.
  3. 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.

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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 .

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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).

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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).

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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.

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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.

While verification of internal consistency is best achieved by built-in checks supported by modeling tools, validation of external consistency requires human inspection and assessment of alternatives. Yet neither will guarantee models 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.

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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.

Further Reading

UML & Users’ Concerns

Objective

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).

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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.

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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:

  1. Those pertaining to objects and activities identified by architectures, whatever their nature: enterprise, systems or platforms.
  2. Those used to describe internals of objects and activities independently of their aspects and behaviors at architecture level.

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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).

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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.

Further Reading

External Links

Agile vs Waterfall: Right vs Left Brain ?

Preamble

All too often when the agile project model is discussed, the debate turns into a religious war with waterfall as the villain. But asking which project model will bring salvation will only bring attrition, because the question is not which is the best but when it is the best. It’s like asking if a hammer is a better tool than a sickle !

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B2C: Balancing Brain

Instead, one should first try to understand how they stand apart, and deduce from that what they are best for; the comparison between the left and right sides of the brain may provide a good starting point.

B2C: Balancing Brain Capacities

If it is (still) impossible to know what people think, it is possible to know where their thinking is rooted in brains, and the answer is unequivocal:

  • The left side of the brain is analytical; faced with a problem, it looks for parts and process them in sequence.
  • The right side of the brain is better at synthesis as it looks at the whole and processes all relevant information simultaneously.

Obviously casts will differ between individuals depending on inborn qualities and developed preferences; moreover, each individual will balance his brain sides according to the task at hand. The same should apply when projects must decide between iterative and procedural approaches.

What a Hand can Hold

When project management is first considered, the Whole vs Parts alternative should be the discriminating factor: since human brains cannot process an unlimited number of elements simultaneously, work units to be handled by teams must be clearly circumscribed, with a number of independent functional units not exceeding a dozen.

That could be a pitfall for agile developments if iterations and increments were to be associated with an exponential growth of complexity. Yet, partitioning a large project into sub-tasks doesn’t necessarily call for a waterfall schema if the sub-tasks can be performed independently.

What the Hand is Told

Sequential processing can be dumb because the intelligence can already be etched in the sequences. That’s not the case if relevant information is to be picked out and processed as a whole; that can only be done with a clear purpose guiding the hand.

Replacing an administrative process by a collaborative one entails some kind of shared ownership, with teams granted full responsibility for decisions, schedules, and quality. Otherwise the different concerns, purposes or authorities, possibly but not necessary at odds, should be set apart as sub-tasks, and milestones  introduced for their consolidation.

What is Handed Over

Development projects may handle three kind of artifacts: texts, models, and code, the first and last being mandatory, the second being optional. Since texts and code are best processed sequentially they are handed over to brain left side; conversely, models are meant to combine different perspectives, e.g structures and behaviors, which put them on the right side of the brain.

Curiously, that seems to put agile in some kind of conundrum: despite models being the symbolic representation best suited to holistic processing, agile approaches are partial to code, even if models are not explicitly ruled out. As a matter of fact, agile tenets are more partial to products than to code, and what is handed over and tested against requirements is not meant to be a program but a running application.

Hand in Hand

Just like the two parts of the brain bring their best to shared concerns and purpose, agile and phased schemes should be enlisted according to their respective merits and shortcomings:

  • Agile is clearly a better option when shared ownership can be secured and milestones and models are not needed.
  • Phased solutions (“waterfall” is a red-herring) are necessary when organizational, functional or technical dependencies between projects mean that some consolidation cannot be avoided between development process.

Assuming agile methods are used whenever possible, models should provide the glue when external dependencies are to be taken into account:

  • Organizational dependencies are managed across model layers: business requirements govern system functionalities which govern platforms implementations.
  • Functional dependencies are  managed across architecture tiers: transient non shared components (aka boundaries) are governed by transient shared components (aka controls) which are governed by persistent shared components (aka entities).
  • Development dependencies should not cross projects limits as they should be managed at domain level using inheritance or delegation.

Further Reading

External Links

Models in Driving Seats

Objective

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:

  1. Inheritance should not cross model layers: hierarchies of business, functional and technical artifacts must be kept separate.
  2. Structures and behaviors pertain to different concerns: abstractions of objects and aspects should be managed independently.
  3. 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.

Further Reading

Requirements Metrics Matter

Objectives

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)
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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.

That can be achieved with functional features assessed along architecture layers.

Requirements should be assessed with regard to their nature and their footprint in functional architecture.
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.

Providing unified OO modeling concepts, metrics can be computed on analysis and design models and set against their counterpart for original requirements. Examples of standard metrics for UML models include:

  • 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.

Further Reading

External Links

 

Projects As Non-Zero Sum Games

Objective

Contracts are as much about collaboration and trust as they are about protection. While projects are not necessarily governed by contracts, all entail some compact between participants. From in-house developments with shared ownership on one hand, to fixed price outsourcing on the other hand, success will depend on collaboration and a community of purpose.

arielSchles_ParTronc
The whole is worth more than its parts (Ariel Schlesinger)

Hence, a first objective should be to prevent prejudiced assumptions and defensive behaviors. Then, taking a cue from Game Theory, compacts should be designed to enhance collaboration between parties by establishing a non-zero sum playground where one’s benefits don’t have to come from others’ losses.

Players and Stakes

Basic participants of engineering projects can be put in two groups, possibly with subdivisions: business units expressing requirements, and engineering units meant to provide solutions.

IntervenantsMatrice
Who’s Who

The former (user, customers, or stakeholder) specify the needs, pay for the outcome, and expect to benefit from its use. Their success is measured by return on investment (ROI) and depends on cost, quality, and timely delivery.

The latter design the solution, develop the components, and integrate them into target environments. Narrowly defined, their success is measured by costs.

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Project participants should collaborate on features and timing.

Hence, while stakes may clearly conflict on prices, there is room for collaboration on features, quality and timing, and that may bring benefits to both customers and providers.

Communication and Collaboration: Modeling Languages

Understanding is a prerequisite to collaboration, and except for in-house developments, there is no reason to expect immediate (aka mediation free) understanding between business and engineering units. Some common language is therefore required if heterogeneous concerns are to be matched.

Catap_NZSG
Mapping functional and engineering concerns.

While in-house developments under shared ownership can proceed with domain specific languages, collaboration between different organizational units along time can only be supported by modeling languages with non specific semantics.

Assessments and Strategies: Requirements Metrics

If participants are to act rationally based on symmetric information, they must agree on some measurements for size and complexity.

Given that collaboration is not to offset interests by nature different or even conflicting, participants’ strategies must be based on shared and reliable information regarding their respective stakes. Moreover, if timing is to matter, projects’ metrics will have to be reassessed periodically, and strategies redefined accordingly.

Catap_NZSG_tmg
Matching the respective deadlines and strategies of customers and providers

Despite clear shortcomings about targets and accuracy, requirements metrics are nonetheless a necessary component of any collaborative framework, with or without explicit contracts.

First of all, some measurements are required to set schedules and plan resources. Even faulty, those estimators will serve their purpose if agreed upon by all participants, with possible biases redressed by consent, and lack of accuracy reduced or circumscribed progressively.

Then, as engineering projects often come with intrinsic uncertainties, they may go off-trail. Providing timely and reliable indicators, participants should be able to anticipate hurdles, reassess strategies, and amend agreements.

Finally, metrics should make room for arbitrage and differentiated strategies; given trustworthy information and clear acceptance criteria, participants would be in a position to weight their respective stakes with risks, rearrange their priorities, and plan their endgames appropriately.

Prices and Acceptance: Contracts

While trustworthy information is a necessary condition for cooperation, it’s not a sufficient one, as players may be tempted to cheat or defect if they think they can get over with it. Leaving out ethical considerations and invasive regulations, the best policy should be to set rewards and sanctions such as to make a convincing case for cooperation benefits.

That can be achieved if participants can opt for differentiated and non conflicting strategies set according  model layers.

Starting with business processes (aka computation independent models) and expected return on investment, customers consider system functionalities (a) and ask providers for feasibility (b), and schedules (c).

Given a functional architecture (aka platform independent models), participants may define strategies: customers associate features with date and expected benefits, providers plan resources and deliveries for products (aka platform specific models and code).

Commitments, by customers on prices and providers on dates, are made at two levels: functional architectures (d) and features (e).

NZSG_0
Business requirements (a), functional architecture (b), developments (c), architectural commitments (d), incremental developments (e).

As for any iterative process, changes must be explicitly circumscribed by invariants, e.g functional architecture of supporting systems. Yet, within those constrains, there may be room for adjustments insofar that providers’ costs and customers’ returns are contingent on schedules.

Processes: Time, Risks, Responsibilities and Milestones

As Einstein put it, “The only reason for time is so that everything doesn’t happen at once”. That seems a perfect fit to project planning, as illustrated by two archetypal project configuration:

  • At one extreme, in-house projects with shared ownership may operate within their own time wraps (aka time-boxes) because decisions by customers and providers can be taken jointly and simultaneously independently of external factors.
  • At the other extreme, outsourced projects are set along sequenced milestones and governed by different organizational units, each with its own time scale. With tasks performed independently and decisions made separately, time becomes a factor and processes are introduced to consolidate time-scales and manage risks and decisions alongside.

More generally, engineering processes are introduced when decisions are to be made along time by different organizational entities. Assuming that time is governed by changes and the decisions to be made thereof, the sequencing of tasks should be defined with regard to the nature of events and decision-making:

Technical: engineering processes come with their own intrinsic constraints regarding the sequencing of operations. When those operations can be performed independently of contexts the whole sequence can be seen as a single step wrapped into a time fold. Otherwise they must be associated with distinct process steps.

Contractual: while uncertainties about contexts may affect both expectations and commitment, decisions have to be made notwithstanding. But decisions carry risks and should therefore be factored out, with rationale and responsibilities stated explicitly. While some responsibilities may be shared, decisions about contexts should remain the sole responsibility of the participants directly in charge and appear as milestones set in contracts.

Managed: whatever the number of model layers and automated transformations, engineering processes set about and wind up with human activities. Given that resources and skills are by nature limited, participants have to make the most of their use. But once milestones are set and technical constraints identified, each participant should be able to optimize its own planning.

That makes for three basic project configurations:

  • Requirements analysis is about business requirements and feasibility. Its main objective is to identify technical and contractual milestones.
  • Use case developments are self-contained projects under shared ownership, bounded by contractual milestones. They come with defined prices and schedules whose mix can be managed by consent.
  • System functionalities are set by cross-cutting objectives and their development is therefore governed by contractual milestones and schedules.

NZSG_1
Architectures and Project Configurations

Managing engineering projects along these principles would greatly improve their integration with business needs on one hand, enterprise architecture on the other hand.

Further Reading

External Links

Requirements Capture

Objective

Requirements are not manna from heaven, they do not come to the world as models. So, what is the starting point, the primary input ?  According to John,  “In the beginning was the word …”, but Gabriel García Márquez counters that at the beginning “The world was so recent that many things lacked names, and in order to indicate them it was necessary to point. ”

 

Frog meditating on requirements capture (Sengai)

Requirements capture is the first step along project paths, when neither words nor things can be taken for granted: names may not be adequately fixed to denoted objects or phenomena, and those ones being pointed at may still be anonymous, waiting to be named.

Confronted with lumps of words, assertions and rules, requirements capture may proceed with one of two basic options: organize requirements around already known structuring objects or processes, or listen to user stories and organize requirements alongside. In both cases the objective is to spin words into identified threads (objects, processes, or stories) and weave them into a fabric with clear and non ambiguous motifs.

From Stories to Models

Requirements capture epitomizes the transition from spoken to written languages as its objective is to write down user expectations using modeling languages. Just like languages in general, such transitions can be achieved through either alphabetical of logographic writing systems, the former mapping sounds (phonemes) to signs (glyphs), the latter setting out from words and mapping them to symbols associated with archetypal meanings; and that is precisely what models are supposed to do.

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Documented communication makes room for mediation

As demonstrated by Kanji, logographic writing systems can support different spoken languages providing they share some cultural background. That is more or less what is at stake with requirements capture: tapping requirements from various specific domains and transform them into functional requirements describing how systems are expected to support business processes. System functionalities being a well circumscribed and homogeneous background, a modeling framework supporting requirements capture shouldn’t be out of reach.

Getting the right stories

If requirements are meant to express actual business concerns grounded in the here and now of operations, trying to apprehend them directly as “conceptual” models would negate the rationale supporting requirements capture. User stories and use cases help to prevent such misgivings by rooting requirements in concrete business backgrounds of shared references and meanings.

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Requirements capture should never flight to otherworldly expectations

Yet, since the aim of requirements is to define how system functionalities will support business processes, it would help to get the stories and cases right upfront, in other words to organize them according patterns of functionalities. Taking a cue from the Gang of Four, three basic categories should be considered:

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

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Products and Usage are two different things

Not by chance, those categories are consistent with the Object/Aspects perspective that distinguish between identities and objects life-cycle on one hand, features and facets on the other hand. They are also congruent with the persistent (non-transactional)/transient (transactional) distinction, and may also be mapped to CRUD matrices.

Since cases and stories will often combine two or three basic categories, they should be structured accordingly and reorganized as to coincide with the responsibilities on domains and projects defined by stakeholders.

User Stories vs Use Cases

Other than requirements templates, user stories and use cases are two of the preferred methods for capture requirements. Both put the focus on user experience and non formal descriptions, with use cases focusing at once on interactions between agents and systems, and user stories introducing them along the course of refinements. That make them complementary:

  • Use cases should be the method of choice when new functionalities are to be added to existing systems.
  • User stories would be more suited to standalone applications but may also be helpful to single out use cases success scenarii.

Depending on circumstances it may be easier to begin requirements capture with a success story (green lines) and its variants or with use cases (red characters) with some activities already defined.

ExecPathsStory
User Stories vs Use Cases

Combining user stories and use cases for requirement capture may also put the focus on system footprint, setting apart the activities to be supported by the system under consideration. On a broader perspective, that may help to position requirements along architecture layers: user stories arise from business processes  set within enterprise architecture, use cases are supported by functional architecture.

Spinning the Stories

Given that the aim of requirements is to define how systems will support processes execution and objects persistency, a sound policy should be to characterize those anchors meant to be targeted by requirements nouns and verbs. That may be achieved with basic parsing procedures:

  • Nouns and verbs are set apart and associated to candidates archetypes for physical or symbolic object, physical or symbolic activity, corresponding container, event, or role.
  • Among them business concerns should point to managed individuals, i.e those anchors whose instances must be consistently identified by business processes.
  • Finally business rules will be used to define features whose values are to be managed at instances level.

Spinning words into archetypes

Parsing nondescript requirements for anchors will set apart a distinctive backbone of clear and straight threads on one hand, a remainder of rough and tousled features and rules on the other hand.

Fleshing the Stories out

Archetypes are like clichés, they may support a story but cannot make it. So it goes with requirements whose meaning is to be found into the intricacy of features and business rules.

However tangled and poorly formulated, rules provide the substance of requirements as they express the primary constraints, needs and purposes. That jumble can usually be reshaped in different ways depending on perspective (business or functional requirements),  timing constraints (synchronous or asynchronous) or architectural contexts; as a corollary, the way rules are expressed will have a significant impact on the functional architecture of the system under consideration.

If transparency and traceability of functional arbitrages are to be supported, the configuration of rules has to be rationalized from requirements inception. Just like figures of speech help oral storytelling, rules archetypes may help to sort out syntax from semantics, the former tied to rules themselves, the latter derived from their targets. For instance, constraints on occurrences (#), collections (*) or partitions (2) should be expressed uniformly whatever their target: objects, activities, roles, or events.

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From rules syntax to requirements semantics

As a consequence, and to all intents and purposes, rules analysis should not only govern requirements capture, it should also shadow iterations of requirements analysis, each cycle circumscribed by the consolidation of anchors:
  • Single responsibility for rule implementation: project, architecture or services, users.
  • Category: whether a rule is about life-cycle, structure, or behavior.
  • Scope: whether enforcement is transient of persistent.
  • Coupling: rules triggered by, or bringing change to, contexts must be set apart.
  • Control: whether enforcement has to be monitored in real-time.
  • Power-types and extension points: all variants should be explicitly associated to a classification or a branching rule.
  • Subsidiarity: rules ought to be handled at the lowest level possible: system, domain, collection, component, feature.

Pricing the Stories

One of the primary objectives of requirements is to put a price on the system under consideration and to assess its return on investment (ROI). If that is straightforward for hardware and off-the-shelf components, things are not so easy for software developments whose metrics are often either pragmatic but specific, or  inclusive but unreliable.

Putting aside approaches based on programs size, both irrelevant for requirements assessment and discredited as development metrics, requirements can be assessed using story or function points:

  • Story points conduct pragmatic assessments of self-contained stories. They are mostly used locally by project teams to estimate their tasks and effort.
  • Functional metrics are more inclusive as based on principled assessment of archetypal system functionalities. Yet they are mostly confined to large organizations and their effectiveness and reliability highly depends on expertise.

Whereas both approaches start with user expectations regarding system support, their rationale is different: function points (FPs) apply to use cases and take into account the functionalities supported by the system; story points (SPs) apply to user stories and their scope is by definition circumscribed. That difference may be critical when categories are considered: points for behavioral, structural and creational stories should be weighted differently.

Yet, when requirements capture is supported both by stories and use cases, story and functions points can be combined to obtain functional size measurements:

  • Story points are used to assess business contents (aka application domain) based on master data (aka persistent) entities, activities, and their respective power-types.
  • Use case points target the part played by the system, based on roles and coupling constraints defined by active objects, events, and controlling processes.
  • Function Points as Use Case Points weighted by Story Points

Non adjusted function points can then be computed by weighting use case function points with the application domain function points corresponding to use case footprint.

Further Reading

A Time For Every Purpose

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

Albert Einstein.

Time and Events

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

Time is what persists of events (S.Dali)

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

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

Time Measurement

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

Time is local but one can take it along

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

Time scales can be designed on purpose

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

Distributed activities have no time of their own

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