Tendencias en Ingeniería de Software - Ing. Grady Booch

8/14/2019 Tendencias en Ingeniería de Software - Ing. Grady Booch

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IBM Research

© 2009 IBM Corporation

Grady Booch

Trends in Software



IBM Research


How Much Software Exists In The World?

SLOC is a measure of labor

– Old code never dies

– Some code is DOA

Some assumptions 1 SLOC = 1 semicolon

Number of software professionals worldwide

% of software professionals who cut code

SLOC/developer/year

$100/SLOC to develop



IBM Research


Number Of Software Professional Worldwide

Number of IT professionals worldwide

y = -128.47x3

+ 12800x2

- 59294x + 146623

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

1 9 4 5

1 9 4 8

1 9 5 1

1 9 5 4

1 9 5 7

1 9 6 0

1 9 6 3

1 9 6 6

1 9 6 9

1 9 7 2

1 9 7 5

1 9 7 8

1 9 8 1

1 9 8 4

1 9 8 7

1 9 9 0

1 9 9 3

1 9 9 6

1 9 9 9

2 0 0 2

2 0 0 5

Number of IT professionals worldwide(assumptions)

Poly. (Number of IT professionals

worldwide (assumptions))



IBM Research


% Of Software Professionals Who Cut Code

% of IT professionals worldwide who cut code

y = -0.0075x + 0.7575

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 9 4 5

1 9 4 7

1 9 4 9

1 9 5 1

1 9 5 3

1 9 5 5

1 9 5 7

1 9 5 9

1 9 6 1

1 9 6 3

1 9 6 5

1 9 6 7

1 9 6 9

1 9 7 1

1 9 7 3

1 9 7 5

1 9 7 7

1 9 7 9

1 9 8 1

1 9 8 3

1 9 8 5

1 9 8 7

1 9 8 9

1 9 9 1

1 9 9 3

1 9 9 5

1 9 9 7

1 9 9 9

2 0 0 1

2 0 0 3

2 0 0 5

of IT professionals worldwide who cut code %)assumptions (

Poly. (% of IT profes sionals worldwide who cut

))code (assumptions



IBM Research


SLOC/Developer/Year

New or modified source lines of code per year per developer

y = -0.0328x3

+ 4.8392x2

- 67.596x + 1062.8

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1 9 4 5

1 9 4 7

1 9 4 9

1 9 5 1

1 9 5 3

1 9 5 5

1 9 5 7

1 9 5 9

1 9 6 1

1 9 6 3

1 9 6 5

1 9 6 7

1 9 6 9

1 9 7 1

1 9 7 3

1 9 7 5

1 9 7 7

1 9 7 9

1 9 8 1

1 9 8 3

1 9 8 5

1 9 8 7

1 9 8 9

1 9 9 1

1 9 9 3

1 9 9 5

1 9 9 7

1 9 9 9

2 0 0 1

2 0 0 3

2 0 0 5

New or modified source lines of code per year

)per developer (as sumptions

Poly. (New or modified source lines of code per))year per developer (assumptions



IBM Research


New Or Modified SLOC/Year And Cumulative

New or modified source lines of code per year per developer & cumulative

0

100,000,000,000

200,000,000,000

300,000,000,000

400,000,000,000

500,000,000,000

600,000,000,000

700,000,000,000

800,000,000,000

1 9 4 5

1 9 4 8

1 9 5 1

1 9 5 4

1 9 5 7

1 9 6 0

1 9 6 3

1 9 6 6

1 9 6 9

1 9 7 2

1 9 7 5

1 9 7 8

1 9 8 1

1 9 8 4

1 9 8 7

1 9 9 0

1 9 9 3

1 9 9 6

1 9 9 9

2 0 0 2

2 0 0 5

New or modified source lines of code per

year

Cumulative source lines of code



IBM Research


The Current State

The typical software-intensive system is

• Continuously evolving

• Connected, distributed, & concurrent

• Multilingual & multiplatform

• Secure & autonomic

• Developed by geographically- temporally-distributed teams

Most systems are actually systems of systems

• Services & other messaging mechanisms dominate

• Such systems encompass both hardware & software



IBM Research


Growth Of Storage

The production of data is growing

• Google processes 20 petabytes/day1

• The Internet handles over 627 petabytes/day2

– Storage densities are increasing

• 200 gigabytes/inch2

are common today• Racetrack memory could increase storage density by two orders of

magnitude (20,000 gigabytes/inch2)3

1http://www.niallkennedy.com/blog/2008/01/google‐mapreduce‐stats.html2http://en.wikipedia.org/wiki/Petabyte3http://www.almaden.ibm.com/spinaps/research/sd/?racetrack



IBM Research


Growth Of Computational Power

Computational power is abundant• A single BladeCenter can reach 7 teraflops• IBM Road Runner has reached one petaflop• Hardware costs are around 20 cents/gigaflop; operating

costs are approximately 3 watts/gigaflop1

– The frequency scaling wars are ending • At 10 atoms/transistor, quantum effects & power dissipation

become critical issues issues• Multicore processors are becoming the norm

1http://en.wikipedia.org/wiki/FLOPS



IBM Research


Growth Of Connectivity

Bandwidth is increasing• Copper may reach 10 gigabytes/second• Wireless networks are becoming pervasive

– Out of 3.7 billion IPv4 addresses1

• China 19.246 million

• US 13.610 million

• Germany 5.414 million

• Italy 3.881 million

• Indonesia 3.465 million

• Taiwan 3.455 million

1http://www.bgpexpert.com/addressespercountry.php



IBM Research


Future Software-Intensive Systems

Future systems will be just like contemporary ones except they

will be

• More massive

• More pervasive

• More transparent• More critical

Domain-specific platforms are growing in dominance



IBM Research


The Role Of Software

Our civilization runs on software (Bjarne Stroustrup)

– Innovating to capture new value

– Improving productivity of resources deployed

The privilege and the responsibility of the software

professional





IBM Research


The Growth Of Complexity

Lower ManagementComplexity

Higher ManagementComplexity

DODweaponsystem

National Air Traffic ControlSystem

Telecom switch

Large-scalesimulation

DODmanagementinformationsystem

Enterpriseinformationsystems

Enterpriseapplication

Business

spreadsheet

Smallscientificsimulation

Embeddedautomotiveapplication Commercial

compiler

Lower Technical

Complexity

Higher TechnicalComplexity





IBM Research


1960s-1970s 1980s-1990s 2000+

Conventional:Diseconomy of Scale

Software Engineering:Diseconomy of Scale

Modern Best Practices:Economy of Scale

P

r o j e c t C o s t

Functionality, Scale, and Complexity

Software Engineering Economics



IBM Research


The Software Development Paradox

Building quality software that matters is fundamentally hard work

– Software development often takes longer than we expect it should

– There never seems to be enough time to do quality work

– There’s always more that remains to be done

Software-intensive systems can amplify human intelligence, but

they cannot replace human judgment

Software-intensive systems can fuse, coordinate, classify, and

analyze information, but they cannot create knowledge



IBM Research


Assembly -> Fortran/COBOL -> Simula -> C++ -> Java

Naked HW -> BIOS -> OS -> Middleware -> Domain-specificWaterfall -> Spiral -> Iterative -> AgileProcedural -> Object Oriented -> Service OrientedEarly tools -> CLE -> IDE -> XDE -> CDEIndividual -> Workgroup -> OrganizationProprietary systems -> Open standards

Languages:

Platforms:Processes:

Architecture:Tools:

Enablement:Ownership:

The entire history of software engineering has been one of

increasing levels of abstraction.

The History of Software Engineering



IBM Research


The Software Development Paradox

Not everything we want to build can be built

– Theoretical/technical/pragmatic limitations

Not everything we want to build should be built

– Economic/social/political limitations



IBM Research


The Limits of Technology

The laws of physics

The laws of software

The challenge of algorithms

The difficulty of distribution

The problems of design

The importance of organization

The impact of economics

The influence of politics

The limits of human imagination

Fundamental

Human



IBM Research


The Developer Experience

Contemporary systems are

– Distributed and concurrent

– Multi-platform and multi-lingual

– Made of hundreds of thousands of moving parts, each of which maybe changed nearly independently

– Involve multiple stakeholders

A sea change is underway

– Code centric -> artifact-centric



IBM Research


Forces In Software



IBM Research


Points Of Friction

Start up

Work product collaboration

Communication

Time starvation

Stakeholder cooperation

Stuff that doesn’t work



IBM Research


How Much Process Is Necessary?

Amount of Process Necessary

When is More Appropriate?Distributed teams

Large projects (25, 125, 625)

Complex projectsExternally imposed constraints

Standards

Contractual requirements

Legal requirements

When is Less Appropriate?Co-located teams

Smaller projects (less than 25)

Straightforward projectsInternally imposed constraints



IBM Research


Architecture First

Conceptual Physical

Process View Deployment View

Logical View

Use-Case View

Implementation View

End-user

Functionality

Programmers

Software management

PerformanceScalability

Throughput

System integrators

System topology Delivery, installation

communication

System engineering

Analysts/Designers

Structure



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Develop Iteratively



IBM Research


How We Got Here

1910s beginning of automation

1920s beginning of expansion

1930s beginning of dependence

1940s beginning of von Neuman machines

1950s rise of the machines

1960s rise of the languages and methods1970s death of the mainframe

1980s age of the personal computer

1990s age of the Internet and new methods

2000s retrenchment



IBM Research


The Future Of Software Engineering

The best way to predict the future is to invent it (Alan Kaye)

General trends

– The movement of software into the interstitial spaces of society

– The growth of complexity

– The primacy of continuously-evolving systems

– The elimination of computational constraints



IBM Research


The Future of Software Engineering

Specific short/medium term issues

– Open source

– Outsourcing

– Active security

– Pattern-centric development

– Service-oriented architectures

– Model-driven development

– Collaborative development environments

Specific longer term issues

– Aspect-oriented programming

– Rise of the non-professional programmer



IBM Research


Where We Are Going

2010s age of transparency

Software burrows itself into the interstitial spaces of society

2020s total dependence

Virtually every human activity touches some software

2030s rise of the machines

Semiautonomous entities with varying degrees of agency amplify humanactivity



IBM Research


The State of Software - 2031

Platforms

Languages

Operating systems & middleware

Connection

Security

Autonomics

Developer experience



IBM Research


Platforms

Moore’s law has died

The typical personal computer contains multiple processors, a

petabyte of main memory, an exabyte of external memory, and

untethered terabit connectivity

Virtual high resolution displays dominate; 3D windows, mice,gestures, and voice are the usual mechanisms for interaction

Form factors will change such that most personal computers will

be wearable or embedded

Most software is embedded in devices



IBM Research


Languages

Most programmers still write algorithmic snippets in the context

of a sea of objects

Legacy XML, Java, C++, and UML persist

Domain-specific frameworks are mainstream

Aspects are mainstream

Some algorithmic breakthroughs have emerged



IBM Research


Operating Systems

Operating systems have largely been commoditized

Middleware that does transaction isolation, load balancing,

resource management, and data access still dominates

– but it too has largely been commoditized



IBM Research


Connection

More than ever, the network is the computer

– Monolithic -> client/server -> Web -> grid

Network access is a global utility

Not everything is an enterprise system, but most applications

are connected to several



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Security

New kinds of cybercrime have arisen

– unlimited piles of money still do not yield secure systems

– Air gaps are still not enough

Rolling failures still plague some systems



IBM Research


Autonomics

No computer has yet passed the Turing Test (but we have come

close)

Most interesting systems exhibit signs of agency and self-repair



IBM Research


Developer Experience

Most developers have grown up believing that the Internet always existed

Most programming is now done by domain-specific developers who only

incidentally learn how to program

– Most development occurs along the edge and the seams of systems

There have been only incremental improvements in programmer productivity and

the programming model – The dominance of Eclipse

– The developer experience is centered around the collaborative development environment

– Distributed development is common

Lawyers are now commonly a part of most development teams

– Some projects are regulated and some individuals are registered



IBM Research


Conclusion

Building quality software that matters is – and will remain -

fundamentally hard

Individuals and teams must cope with high degrees of

uncertainty, ambiguity, and chaos, while at the same time,

demanding creativity, predictability, and repeatability

We can only hope to engineer the illusion of simplicity

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Tendencias en Ingeniería de Software - Ing. Grady Booch