Bern.jb

Should we Resurrect Software Engineering?

Jean Bézivin
JBezivin@gmail.com

CHOOSE Forum 2012, Bern

07/01/2013 CHOOSE Forum 2012, 14 December 2012, Bern 1

Requiem for Software Engineering


You escaped these titles

Software Engineering is Dead; Long Live
Software Engineering
The basis of Software Engineering 2.0
From Software Engineering to Engineering
Software
Towards a Unified Theory of Engineering
Is Generic Engineering Feasible?
How to Bridge Problem Spaces and Solution
Spaces?
Some remarks about eEngineering


Agenda

1. Introduction
2. Software Engineering is Dead
3. Model Driven Engineering Missed the Boat
4. Problem and Solution Spaces
5. Domain Engineering
6. Support Engineering
7. Conclusion


Sorry for the bad news

SOFTWARE ENGINEERING
IS DEAD


… or at least critically ill

The NATO Conferences of 1968 and  Last time we heard good news
1969 were motivated by the belief was in the 80’s with the
that software development should be invention of the Smalltalk
"based on the types of theoretical Browser
foundations and practical disciplines  Every year since then, we have
that are traditional in the established been eagerly waiting for better
branches of engineering.“ health news at OOPSLA, ECOOP,
ICSE, etc.
Surprisingly the conferences did not
discuss what these foundations and  But unfortunately we had only
disciplines were, or how they could patterns, aspects, Java, and false
be emulated. There has been little
hopes that did not last very long
discussion of this topic in the  Adding superficial deltas to the
intervening forty years and more. J-language is boring
Some important lessons have been  And now many people have
neglected. given up, to concentrate only on
the problem of organization of
agile teams
From Michael Jackson’s Web site


Hype after Hype

 Are we condemned to jump from hype to
hype like a fashion industry?(1)
 What is the hidden meaning in the long
term evolution of our discipline?

Google Ngram
Viewer

(Raw Ngram
buzzword observations)

(1)
Ivar Jacobson

Software Engineering as a Succession of Hypes

 Many developers’ career
paths follow a continuous
zigzag from hype to hype,
from objects to models,
from models to services, ...
 We need to focus more on
long term continuity and
progresses than on small
ruptures and failures.
 Progress in SE? What is the
deep meaning, if any, behind
this succession of hypes?


OOPSLA: An Object Odyssey

[OOPSLA] became the forum for some of the most
important software developments over the last couple of
decades. OOPSLA was the incubator for CRC cards, CLOS,
Design Patterns, Self, Agile Methodologies, Service-
Oriented Architectures, Wikis, Unified Modeling Language
(UML), Test Driven Design (TDD), Refactoring, Java,
Dynamic Compilation, and Aspect-Oriented Programming,
to name just some of them.

Probably the Palo Alto Research Center produced more
results in ten years between 1970 and 1980 that the
OOPSLA community in 25 years between 1986 and 2012.

Design Patterns: Kent Beck and Ward
Cunningham


Not a competition

 We are suffering from the « My solution is better than yours » syndrome.
 Where is the big picture in this hype-after-hype sequence? Will some
technology finally prevail?
 Let us focus more on technology cooperation than on technology
competition.


What has changed in the past 50 years ?

 Expressions like “CAD” or “Computer Assisted” or
“Computer Aided” have lost all their discriminant
meaning in engineering

 Most engineering fields are now using
computers and software

 Time to adapt our vision


Model Driven Engineering

MDE MISSED THE BOAT


Models Have Failed

 “Models” have failed, at least temporarily.
 Deployment of MDE seems today to have reached a standstill.
 Immense hopes greeted the MDA™ initial proposal as a possible
way to regenerate the entire software engineering practices
 Recognition today that its impact is rather limited and its perspectives
quite confined.
 Observation that the process is not at the same level that in case of
technology take-off like OT in the 80’s
 Unfortunately this last silver bullet fired blank
 What went wrong?
 No sustainability
 lack of model portability in time and space
 No killer app
 Decreasing confidence in UML
 Many other factors


Separating the platform independent and
dependent parts of a system (PIM/PSM)
We don't want anymore to pay such a high price for
simply moving our information system to a new
middleware platform (COM, CORBA, Java, HTML,
XML, DotNet, etc.) when our business system stays
stable.
We are prepared to pay a last price for building the
abstract models of our business and services that
will guarantee us against technological
obsolescence.
From there, any platform provider will also have to
provide the mapping solutions from standard
business models before we buy.

November 2000


Write Once, Run Anywhere
Model Once, Generate Anywhere
From Platform Independent
Multi-target Models to Platform Specific
code generation Models
PIM PIM to PSM

etc.

Service architecture,
CORBA SMIL/Flash Cloud computing,
…
Java/EJB
C#/DotNet

Web/XML/SOAP + SVG, GML, Delphi, ASP, MySQL, PHP, etc.

07/01/2013 CHOOSE Forum 2012, 14 December 2012, Bern 15 - 15 -

Sustainable Software?

 Sustainability is the new hype, but is this
hype sustainable?
 The first promise/commitment of MDA™
was on sustainability:
 “Developers gain the ultimate in
flexibility, the ability to regenerate code
from a stable, platform independent
model (PIM) as the underlying
infrastructure shifts over time”.
 This was based on the “hidden
condition” that the PIM was written in
UML, a language supposed itself to be
stable over long periods of time.

PERMANENT LINK TO THIS COMIC: HTTP://XKCD.COM/1007/
IMAGE URL (FOR HOTLINKING/EMBEDDING):
HTTP://IMGS.XKCD.COM/COMICS/SUSTAINABLE.PNG


Nice Gems … but is this core MDE?


Some MDA success stories but no killer app. yet

http://www.omg.org/mda/products_success.htm

What is a Killer App?

Tom Love experiment
at Schlumberger
(see also the Analyst
At Xerox)

220 lines of Smalltalk
vs.
10,000 lines of Fortran

A killer app. should provide measurable and reproducible evidence that the new proposal offers an order of magnitude
improvement over previous solutions.


UML and MDE: friend or foe, devil or angel?

 UML was the conclusion of the
OOADTF and the beginning of MDA
 Everything started with UML and this is
probably the main problem of MDE
 UML is a loosely defined language
 UML is a language with one syntax and an
infinity of semantics (Very popular BYOS)
 UML is not a badly designed language
 Because it was never designed at all
 It is the result of industrial consensus,
obtained through a precise process
(committee invention)
 Bad modularity principles (profiles)
 UML as a visual syntax for C++
 UML as a better « programming » language?


UML as the modeling language archetype?

 UML considered as the archetype of modeling languages, often
illustrates properties that are at the extreme opposite of the main MDE
basic principles.
 UML as the typical visual language.
 Many still wrongly associate MDE with visual modeling.
 MDE has later shown that textual modeling (like in Xtext) is often better than visual
modeling.
 UML as a general purpose language (GPL).
 MDE has demonstrated the interest of precise and focused Domain Specific
Languages (DSLs).
 UML as an OO modeling language
 MDE has demonstrated the benefits of multiparadigm modeling, considering not
only objects, but rules, events, functions, tables, processes, etc.
 UML has wrongly conveyed the idea that modeling was only OO modeling
 UML is known for its complexity, by the size of its metamodel and its rapid
evolution
 MDE promotes the idea of simple languages that could be combined, with controlled
execution


Confusing Executability and Precision

 One important ambiguity has been to let the idea
propagate that all MDE-models, (including UML), could be
made executable.
 MDE promotes the idea that some models may be
executable but not all
 A perverse corollary of this is that non executable models are not
precise
 Many models may be executable and however very precise
 Precision is not always obtained through computer executability


XMI Failure

 From SMIF to XMI, a good start.
 XMI as the final interoperability solution, the first mistake
 The proportion of native data expressed in XMI is
completely marginal and will not increase.
 Through its various versions, XMI added mess to mess.
 XMI with UML is part of MDE legacy.
 XMI will eventually disappear, creating an additional
problem of maintenance for UML models.


Lack of Theory

Squares and Circles

The «real» World The World of «Models»

a system S a model M


Representation and Conformance

The two orthogonal Metamodel
dimensions of MDE

wrt
conformsTo

System Model
representationOf


Taking the representation relation seriously

"What about the [relationship between model
and real-world]? The answer, and one of the
main points I hope you will take away from this
discussion, is that, at this point in intellectual

relationship". [Brian Cantwell Smith]
history, we have no theory of this [...]

Where are models
coming from?


Action on a model is not action
on the system (real world)

repOf
a system S a model M

A situation or a phenomenon
of the real or imagined world.


But we Learned a Lot with MDE

 Any model M represents a system S  A transformation is a model
1. Representation principle 6. HOT principle

 A system S may be represented by  Abstract correspondences between
2. Multiple view principle 7. Weaving principle

several models models are represented as models

 Any model M conforms to the  Model elements may be considered
3. Conformance principle 8. Megamodel principle

language of its metamodel MM as models

 Any metamodel MM conforms to a  All models specialize a common
4. 3-level principle 9. Unification principle

common metametamodel MMM abstract model

 The most important operation  Any model has a given
5. Transformation principle 10. Technical Space Framework

applicable to models is a representation defined by its
transformation technical space


What is a modeling language?

 The expression “modeling  Examples
language” is recent
 Flowcharts (~1950)
 Examples:
Requirements  Petri Nets (~1960-1970)
Abstract Specifications  PSL/PSA (~1967)
Formal Methods
 State Diagrams (~1967)
•

 SADT (~1969)
Semi-Formal Notations
•

Early aspects (?)
•

etc.  DFD (~1975)
•

 The expression “modeling  Entity-Association
•

language” is recent (Chen, ~1976)
•

 Tentative definition: A modeling
language is a language  JSD (~1982)
contributing to software  AD/Cycle (~1982)
production, maintenance or
operation that is not directly  UML (~1996)
executable.  MDA (~2000)


What are the relations between
Programming & Modeling Languages?


The two parallel tracks

Programming
Lisp Algol60 Smalltalk
Fortran COBOL PL/1 Java
Ruby Javascript Scala
Dart
Languages
ADA C++ Python
Assembler Prolog Pascal C# F# Go

No global consolidated history of Modeling Languages ExecutableUML?

Flowcharts Petri JSD Z B UML Modeling
SREM SADT SBVR
SART DFD VDM OMT SysML Languages
Sara PSL/PSA


Evolving scope
Not only for code generation
[If MDE is the Solution, then what was the Problem?]

MDD

MDE

MDE

MDD

MDD = Model Driven (Software) Development MDE = Model Driven Engineering


MDE is not only for code generation

 Restricted focus
 MDE concentrated too much
Initially MDA was for just software engineering,

on models of code and not
and the scope was progressively extended

enough on models of data
on models of solutions and
Model

not enough on models of
Driven

problems
Engineering

on Information Systems
models and not enough on
appliesTo

Business models
on modeling in the small and
not enough on modeling in
the large
Software Data System Business

 etc.
Engineering Engineering Engineering Engineering

UML/SPEM CWM SysML BPMN


The engineer view of « how to solve it? »

PROBLEM AND SOLUTION SPACES


Technical Spaces
Technical Space: A representation system for models
and a set of technical solutions to handle them. A
framework usually based on some algebraic structures
(relational tuples, trees, graphs, hypergraphs, etc.)
aTechnicalSpace

System: A group of interacting, MetaMetaModel
interrelated, or interdependent
elements forming a complex whole.

aSystem aModel
repOf

Model: An abstract representation of a
system created for a specific purpose.


Structuring the solution space

Problem How to Solve it? Problem
Space

Java XML Ontologies etc.
MDA™ DBMS

Solution
Space
Grammars XML Schema Metamodel

Programs XML Documents Model

All models are not MOF or ECORE Models.


Three representations for the same program

Java Java JavaML
Grammar Metamodel schema

Java Java JavaML
Program Model Document

Program TS MDA TS XML TS


Projections between EBNF, MDE and XML

XML TS MDE TS EBNF TS

M3 XSD.xsd MOF EBNF.g

M2 JavaML.xsd Java Java.g

M1 Test.xml Test.xmi Test.java


Ubiquitous Software:
From Problems to Solutions

Domain
Problem Spaces Engineering

Mappings

Solution Spaces
Support
Engineering


Focus on Engineering

Scientists study the world as it is; engineers

Theodore von Kármán
create the world that has never been.


The two spaces

Domain
Engineering
Problems lie here

Support
Engineering
Tools to solve problems
may be found here


Problems and Solutions
Support Engineering (vertical?)
Process engineering


Product (line) engineering


Software language engineering


Model engineering


Service engineering


Data engineering


Program engineering


Event engineering


Constraint engineering


System engineering


Requirement engineering


Ontology engineering


Legal engineering
Domain Engineering (horizontal?)



Civil engineering


Building engineering


Electrical engineering


Mechanical engineering


Business engineering


Biological engineering


Automotive engineering


Health engineering




Problem Spaces

DOMAIN ENGINEERING


Domain Engineering

 Similar processes across all engineering
fields

1. Build abstract models
Using some given ontologies
For example mechanics, electronics, etc.


2. Verify/Validate Abstract Models


Using some validation technique

3. Put into production


Create Products from Models
Automatic, Semi-automatic or Manual


4. Put into operation


Deployment
Augment or change the real world


Adding a new bridge, a new phone device, a
new building, a new operational program,


etc.



Electrical Engineering

Augmenting,
Building Validation Putting in
Changing the
abstract models Verification Production
world


Construction Engineering

Augmenting,
Building Validation Putting in
Changing the
abstract models Verification Production
world


Old and new engineering fields


Many Communities, Many Journals
Healthcare Engineering
Biomedical engineering
Computer-aided medical engineering
Medical/disease modeling
Rehabilitation engineering
Healthcare energy systems engineering
Healthcare support service engineering
Emergency response engineering
Engineering issues in public health and epidemiology
Aging Engineering and aging (elderly patient service)
Healthcare engineering education
http://www.govengr.com/ …

Concurrent engineering is
Neural
a work methodology based
engineering (also
on the parallelization of tasks
known as
Neuroengineering) is a (i.e. performing tasks
discipline concurrently). It refers to an
within biomedical approach used in product
engineering that uses development in which
engineering techniques functions of design
to understand, repair, engineering, manufacturing
replace, enhance, or engineering and other
otherwise exploit the functions are integrated to
properties of neural reduce the elapsed time
systems
Journal of Neural Engineering to help scientists, clinicians and engineers
required to bring a new
to understand, replace, repair and enhance the nervous system. product to the market.


And many more


Synergies Between Engineering Fields
Program Engineering Building Engineering

Once in a great while, a great idea makes it across the
boundary of one discipline to take root in another. The
“The Origins of Pattern Theory,
adoption of Christopher Alexander’s patterns by the
the Future of the Theory, And The software community is one such event.
Generation of a Living World”
Christopher Alexander .
Jim Coplien


Domain Engineering

Domain
Problem spaces Engineering

Product Process
Engineering Engineering
Solution spaces
(Support Engineering)


Many features common
to all domain engineering fields
 Based on support engineering
 Product & Process focus
 Including HR and team management
 Chain
 Building Abstract Models
 Verification/Validation
 Putting in Production
 Putting in operation
 Need for a strong model repository
 Scaling up to millions of parts
 Cooperative concurrent access
 Point of view mechanisms
 Strong zooming mechanisms


Solution Spaces

SUPPORT ENGINEERING


Variety Complexity of the
Support Engineering Landscape

Language Program Ontology Model

Web Service Transformation Rule

Complex Event Data Process Product

HR Team Software Other


Specialized engineering fields

Language
Engineering

Software
Language
Engineering

Grammar Model Ontology
Engineering Engineering Engineering


Composite engineering fields


Model Engineering
Same visual notation,
different context,
different meaning
Model element (Thick red dotted lines
for bicycle lanes)

Metamodel element

µ
Metamodel

c2
The legend
Model is the metamodel


Process engineering

encompasses a vast range
Process Engineering
 Process engineering

of industries, such as
chemical, petrochemical,
mineral processing,
advanced material, food,
Software Process
Engineering
pharmaceutical,
biotechnological, and
software industries.
 See also Concurrent
SPEM
Engineering


PSL (Process Specification Language)
Process Specification Language NIST

Process
+name

1
0..*

Activity
+preceding
-name
-duration
0..*

0..* +following


Early SPEM (UPM)


Team and Product management

Team Management Product Lifecyle
Engineering Management (PLM)

Software Team
Product Line
Management
Engineering
Engineering

Software Product
Agile Methods
Line Engineering


Data Engineering


Program Engineering

Short name: “Programming”
Long tradition of excellence
Noble and visible part of SE
Very difficult
Many iterations and branches
Structured Programming
OO Programming
Functional Programming
Etc.


What is a program?
A
Programming
The World Language

(domain)
c2

The Application
Requirements A Program
class Application{
(use cases)
public static void main (String[] args) {
System.out.writeln("Hello, world")
class BankCustomer{

}
}

}
class Printer{
}
The Machine class Application{
(platform) }


Making implicit relations explicit

 Language engineering is
related to the definition
and handling of
Language languages
 Program engineering
Engineering

deals with the use of
?

Program executable software
Engineering languages to produce
operational programs
?

that will participate in
human activities
(includes deployment).


Models for producing programs

Domain
(World)

Problem
(Application) Program
Today
Knowledge in the head of programmer

Platform
(Machine) Models

Other aspects
Tomorrow
From implicit to explicit
Transformation based

Code generation with models


But also models for understanding programs

View1

Program View2
Today

Models View3

Tomorrow

Code understanding with models


Sound terminologies are always useful

 Good definitions allow avoiding sterile, futile,
and non productive discussions
 «Mal nommer les choses, c'est ajouter au
malheur du monde» Albert Camus
[To misname things is to add misery to the world]
Programming
is Modeling

Model
Engineering
?

Program
Programming
Modeling is

Engineering
?


Many Possible Useful Collaborations
Between Support Eng.
Service Data Process Product

Model Model

Program Language Transformation Data

Model Model


Combining Support Engineering

Model
Engineering
?

Service
Engineering
?

1,400,000 results
N defs of MDA
M defs of SOA
P ways to combine them


Understanding complex relations

Business
Engineering
BPMN ?

Software
Engineering
? UML

Model Language Data Service


Software Engineering is Engineering

CONCLUSIONS


Let’s forget about “Computer Science”

Program Language

Computer
Engineering ?
Informatics
Data
Engineering

Platform Software


Conclusions

After nearly 50 years
Software engineering is dead
MDE missed the boat
No other major silver bullet on the horizon
Good time to invent a new future
SE as a branch of generic eEngineering (eE)
eE using most of the ideas of MDE
eE taking inspiration of the brightest ideas in
other domain or support engineering


Software Engineering and Engineering Software

Engineering

eEngineering

Electrical
Software Engineering Civil Engineering
Engineering


Unified Theories of Engineering

 Yes we need to resurrect Software Engineering.
 The expression “Software Engineering” was coined in 1968.
 In 2018, for the 50th anniversary, a new “NATO-like” event to
refund SE2.0 on solid grounds, taking into account what has
been learnt in half-a-century?
 We need to invent SE2.0, in a radical departure from
what has been done in the past 50 years.
 The problem is not to invent a marginally better
programming or modeling language.
 SE2.0 could/should be just be a specialization of
eEngineering, a generic view of modern engineering
practices.
 Short term proposal: a generic platform for eEngineering


Thanks

• Questions?
• Comments?

JBezivin@gmail.com


Bern.jb

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