Scrum Theory

Agile Project Management With SCRUM: Theory and Practice


Jeff Sutherland, Ph.D.Chief Technology Officer

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http://jeffsutherland.com


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The Zen of SCRUM So simple, anyone can implement it! So easy, all can benefit! So subtle, few achieve transcendent performance … How is it that a project manager does nothing, and achieves

everything? Interlocking principles emerge product like jigsaw puzzle. Novice removes one piece -- engine never fires on all

cylinders … Who can know why? ScrumMaster must understand deeply and practice

rigorously. Only then will team members say, “This experience changed

my life!”

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Complex Adaptive Systems (cas) Interacting agents respond to stimuli. Stimulus-response behavior is defined in terms of rules. Agents adapt by changing rules as experience accumulates. Agents aggregate into meta-agents whose behavior is emergent. How can a collection of dumb things emerge smart system behavior?

Maamar, Zakaria and Sutherland, Jeff (2000) Toward Intelligent Business Objects: Focusing on Techniques to Enhance Business Objects that Exhibit Goal-Oriented Behaviors. Communications of the ACM 40:10:99-101.

Frozen

ChaosFragmentation

casSelf Organization

Web services?

1998 Agents1995 Components1993 Business Objects

1980 Classes1970 Procedures

Enterprise Systems are cas

Business entities are examples of complex adaptive systems.

Modification time is on the order of months or years, roughly time required to change software.

Automating business processes renders parts of the business in software.

Business systems have severely constrained rule sets, making ideal test bed for cas concepts.

Sutherland, Jeff and van den Heuvel, Willem-Jan (2002) Developing and integrating enterprise components and services: Enterprise application integration and complex adaptive systems. Communications of the ACM 45:10:59-64.

Objects Meet Requirements for Evolution

Variation: there is a continuing abundance of different elements (class libraries).

Heredity or replication: the elements have the capacity to create copies or replicas of themselves (inheritance).

Differential "fitness": the number of copies of an element that are created in a given time varies, depending on interactions between the features of that element and the features of the environment in which it persists (reuse).

Daniel Dennett. Darwin's Dangerous Idea: Evolution and the Meanings of Life. Simon and

Schuster, 1995, p. 343.

http://www.amazon.com/exec/obidos/ISBN=0684802902/jeffsutherlasobjA/

Do Programmers Meet Evolutionary Requirements?

Algorithms create movement through design space – Simple minded repetitive procedure – Incremental change to adjacent designs

"Cranes" accelerate movement through design space – Sex and genetic engineers – Evolutionary prototyping and smart people– Emergent architecture

Brooks, Fred. No Silver Bullet: Essence and Accidents of Software Engineering. IEEE Computer, Vol. 20, No. 4, April 1987, pp. 10-19.

Change is Imperative:Wasserman's 7 Factors Driving Change

Criticality of time to market Shift in computing economics Powerful desktop computers Extensive networks and the Web Growing availability of object technology WIMP (windows, icons, menus, pointers) Unpredictability of the waterfall model of software

development

Tony Wasserman. Toward a Discipline of Software Engineering. IEEE Software 13:6:23-31, Nov 1996.

Too many projects fail

"Why Are Systems Late, Over Budget, Wrong?""The Waterfall Methodology!" (Paul Bassett)

Analysis Paralysis – static modeling overused – specs are stale baked

Design-from-Scratch – no generic models – no standard architectures

Large Project Teams User Intermediaries No Early Warning Signals

Bassett, Paul G. Framing Software Reuse: Lessons from the Real World. Yourdon Press Computing Series, 1997.

http://www.amazon.com/exec/obidos/ISBN=013327859X/jeffsutherlasobjA/

Wicked Problems: Righteous SolutionsOut of a total cost of $37B for the sample set, 75% of [DOD] projects failed or were never used, and only 2% were used without extensive modification. Jarzombek. The 5th Annual JAWS S3 Proceedings, 1999.

Wicked problems have no definitive formulation. Each attempt at creating a solution changes your understanding of the problem.

Wicked problems have no stopping rule. The problem-solving process ends when resources are depleted, stakeholders lose interest or political realities change.

Solutions to wicked problems are not true-or-false, but good-or-bad … getting all stakeholders to agree that a resolution is "good enough" can be a challenge.

There is no immediate or ultimate test of a solution to a wicked problem. Every implemented solution to a wicked problem has consequences. Wicked problems don't have a well-described set of potential solutions. Various

stakeholders have differing views of acceptable solutions. Each wicked problem is essentially unique. Part of the art of dealing with wicked

problems is the art of not knowing too early what type of solution to apply. Each wicked problem can be considered a symptom of another problem. A wicked

problem is a set of interlocking issues and constraints that change over time, embedded in a dynamic social context.

The causes of a wicked problem can be explained in numerous ways. The planner (designer) has no right to be wrong.

Rittel, H and Webber M. Dilemmas in a General Theory of Planning. Policy Sciences, Vol. 4. Elsevier, 1973.Degrace and Hulet's book, Wicked Problems, Righteous Solutions, Prentice Hall, 1990

Software Development is an Empirical Process

Ziv's Uncertainty Principle in Software Engineering - uncertainty is inherent and inevitable in software development processes and products [Ziv, 1996].

Humphrey's Requirements Uncertainty Principle - for a new software system, the requirements will not be completely known until after the users have used it.

Wegner's Lemma - it is not possible to completely specify an interactive system [Wegner, 1995].

Productivity: All at Once Models Single Super-Programmer Handcuffing two programmers

together Brook’s Surgical Team Borland Quattro project Goldratt’s “The Goal” Senge’s systems thinking Holland’s complex adaptive systems

Team Size: Development Effort in Months

The smaller the better. 491 medium sized projects with 35,000-95,000 SLOC (source

lines of code)Putnam, Lawrence H. and Myers, Ware. Familiar Metrics Management: Small is Beautiful--Once Again. IT Metrics Strategis IV:8:12-16, Cutter Information Corp., August 1998.

Team Size: Development Time in Months

Sweet spot is 5-7 people 491 medium sized projects with 35,000-95,000 SLOC

(source lines of code) Putnam, Lawrence H. and Myers, Ware. Familiar Metrics Management: Small is Beautiful--Once Again. IT Metrics Strategis IV:8:12-16, Cutter Information Corp., August 1998.

Bell Labs Report on most productive project ever: Borland Quattro for Windows

1,000,000 lines of C++ code

BWP Industry standard

Time in months 31 >50

Staff 8 >100

Function points per staff month

77 2

BQW

Jones, Capers. Applied Software Measurement, Second Edition. McGraw Hill, 1997.

James Coplien. Borland Software Craftsmanship: A New Look at Process, Quality, and Productivity. Proceedings of the 5th Annual Borland International Conference, Orlando, 1994.

One of most remarkable organizations, processes, and development cultures seen in AT&T Bell Laboratories Pasteur process research project

Project management, product management, QA integral to team, all making technical contributions

Higher communication saturation than 89% of projects More even distribution of workload “Anti-schismogenetic” – no cliques Highly iterative development Strong architectural interaction with implementation More time spent in project team meetings than anything else –

several hours a day Gerry Weinberg notes that CMM Level 1 and 2 teams need strong

managerial direction. Level 3 paradigm shift is self-directing team. Borland team was clearly in this category, although not by commonly accepted criteria.

http://www1.bell-labs.com/user/cope/coplien.tiff.gz

Team comments on Quattro project

“We are satisfied by doing real work.” “Software is like a plant that grows. You can’t

predict its exact shape, or how big it will grow.” “There are no rules for this kind of thing—it’s

never been done before.”

“Evolutionary development is best technically, and it saves time and money.”Report of the Defense Science Board Task Force on Military Software. Oct 1987.

History of Iterative and Incremental Development (IID)

1956 – Benington’s stagewise model – USAF SAGE System

1957 – IBM Service Bureau Corp, Project Mercury, IBM Federal Systems Devision – Gerry Weinberg

1960 – Weinberg teaching IID at IBM Systems Research Institute

1969 - Earliest published reference to IID:– Robert Glass. Elementary Level Discussion of

Compiler/Interpreter Writing. ACM Computing Surveys, Mar 1969

Larman, Craig and Basili, Vic. A History of Iterative and Incremental Development. IEEE Computer, June 2003 (in press)

History of Iterative and Incremental Development (IID) 1971 – IBM Federal Systems Division

– Mills, Harlan. Top-down programming in Large Systems. In Debugging Techniques in Large Systems. Prentice Hall, 1971

1972 – TRW uses IID on $100M Army Site Defense software 1975 – First original paper devoted to IID

– Gasili, Vic and Turner, Albert. Iterative Enhancement: A Practical Technique for Software Development. IEEE Transactions on Software Engineering. Dec 1975.

1977-1980 – IBM FSD builds NASA Space Shuttle software in 17 iterations over 31 months, averaging 8 weeks per iteration

– Madden and Rone. Design, Development, Integration: Space Shuttle Flight Software. Communications of the ACM, Sept 1984.


History of Iterative and Incremental Development (IID) 1985 – Barry Boehm’s Spiral Model

– Boehm, Barry. A Spiral Model of Software Development and Enhancement. Proceedings of an International Workshop on Software Process and Software Environments. March, 1985

1986 – Brooks, Fred. No Silver Bullet. IEEE Computer, April 1987– Nothing … has so radically changed my own practice, or its effectiveness [as

incremental development]. 1994 – First SCRUM at Easel Corporation

1994 – DOD must manage programs using iterative development– Report of the Defense Science Board Task Force on Acquiring Defense

Software Commercially. June 1994. 1995 – Microsoft IID published

– McCarthy, Jim. Dynamics of Software Development. Microsoft Press, 1995. 1996 – Kruchten. A Rational Development Process. Crosstalk. July.

– Origins of RUPLarman, Craig and Basili, Vic. A History of Iterative and Incremental Development. IEEE Computer, June

2003 (in press)

History of Iterative and Incremental Development (IID) 1996 – Kent Beck Chrysler Project

– Origin of XP 1996 – Larman meets with principal author of DD-STD-2167

– David Maibor expressed regret for the creation of the waterfall-based standard. He had not learned of incremental development at the time and based his advice on textbooks and consultants advocating the waterfall method. With the hindsight of iterative experience, he would recommend IID.

1997 – Coad and DeLuca rescue Singapore project– Origin of Feature-Driven Development

1998 – Standish Group CHAOS Project– Top reason for massive project failures was waterfall methods. “Research

also indicates that smaller time frames, with delivery of software components early and often, will increase success rate.

1999 – Publication of extensive DOD failures– Out of a total cost of $37B for the sample set, 75% of projects failed or were

never used, and only 2% were used without extensive modification. Jarzombek. The 5th Annual JAWS S3 Proceedings, 1999.


History of Iterative and Incremental Development (IID) 2001 – 17 process expert “anarchists” meet at

Snow Bird

– Agile Manifesto initiated 100s of books and papers on agile development

2001 – MacCormack’s study of key success factors

– MacCormack, Alan. Product-Develoment Practices that Work. MIT Sloan Management Review 42:2, 2001.


Manifesto for Agile Software DevelopmentWe are uncovering better ways of developing software by doing it and helping others do it.

Through this work we have come to value:

Individuals and interactions over processes and tools Working software over comprehensive documentation

Customer collaboration over contract negotiation Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

MacCormack Process Evolution

Waterfall model – sequential process maintains a document trail

Rapid-Prototyping Model – disposable prototype helps establish customer preference

Spiral Model – series of prototypes identifies major risks

Incremental or Staged Delivery Model – system is delivered to customer in chunks

Evolutionary Delivery Model – iterative approach in which customers test an actual version of the softwareMacCormack, Alan. Product-Development Practices That Work: How Internet Companies Build Software. MIT Sloan Management Review 42:2:75-84, Winter 2001.

MacCormack Success Factors

Early release of evolving product design to customers. Daily incorporation of new software code and rapid

feedback on design changes A team with broad-based experience in shipping

multiple projects Major investment in design of product architecture

MacCormack, Alan. Product-Development Practices That Work: How Internet Companies Build Software. MIT Sloan Management Review 42:2:75-84, Winter 2001.

SCRUM Origins: Takeuchi and Nonaka Lessons from Fuji-Xerox, Canon, Honda, NEC, Epson, Brother, 3M, Xerox, HP

Old model – Relay Race

– Speed and flexibility not adequate in today’s market

New model - Rugby

Takeuchi, Hirotaka and Nonaka, Ikujiro. 1986. The new new product development game. Harvard Business Review 64:1:137-146 (Jan/Feb), reprint no. 86116.

Moving the SCRUM downfield

http://jeffsutherland.com/Documents%20and%20Settings/jsutherland/My%20Documents/My%20Music/adidas_haka_ad.mov

Takeuchi and Nonaka Success Factors

Built-in instability Self-organizing project teams Overlapping development phases “Multilearning” Subtle control Organizational transfer of learning

“These characteristics are like pieces of a jigsaw puzzle. Each element, by itself, does not bring about speed and flexibility. But taken as a whole, the characteristics can product a powerful new set of dynamics that will make a difference.”

Factor 1: Built-in instability Top management kicks off development process by signaling

broad goal. Project team is offered extremely challenging goals with wide

measure of freedom.

– Example: Fuji-Xerox gave FX-3500 project team two years to come up with a copier that cut costs in half

Top management creates an element of tension in the project team through challenging requirements with wide freedom to achieve strategic objective.

– Honda Executive: “It’s like putting the team members on the second floor, removing the ladder, and telling them to jump or else. I believe creativity is born by pushing people against the wall and pressuring them almost to the extreme.”

Factor 2: Self-organizing project teams

A project team takes on a self-organizing character as it is driven to a state of “zero information” – where prior knowledge does not apply.

Left to stew, the process begins to create its own dynamic order. The project team begins to operate like a start-up company. A group possesses a self-organizing capability when it exhibits

three conditions.

– Autonomy

– Self-transcendence

– Cross-fertilization At some point, the team begins

to create its own concept.

ScrumDown at the Radcliffe Rugby Club

http://www.hcs.harvard.edu/~radrugby/

Autonomy

Headquarters involvement is limited to providing guidance, money, and moral support at the outset.

On a day to day basis, management seldom intervenes and the team is free to set its own direction.

In a way, top management acts as a venture capitalist

– “We open our purse and keep our mouth closed.”

Example: IBM development of personal computer Example: Honda City project team, average age 27,

“Develop the kind of car that the youth segment would like to drive.”

Self-transcendence The project teams appear to be absorbed in the never-

ending quest for “the limit.” They elevate their goals through the development

process. By pursuing what appear to be contradictory goals, they

devise ways to

override the status quo and

make the big discovery. Example: Canon AE-1 team

Flyhalf Sarah Schooler of Radcliffe fending off Boston College


Cross-fertilization

Team with wide variety of specializations, thought processes, and behavior patterns carries out new product development.

Working in one large room is best (Fuji-Xerox). “When all team members

are in one room, others

information becomes yours

without even trying.”

Radcliffe Rugby Football Club


Factor 3: Overlapping Development Phases Self-organizing character of the team produces unique

dynamic or rhythm Sashimi system – Fuji Xerox Rugby system – Honda Hard merits (demerits)

– Speed and flexibility (watch out for muck and mall)

Soft merits

– Share responsibility and cooperation

– Stimulates involvement and commitment

– Sharpens a problem-solving focus

– Develops initiative and diversified skills

– Heightens sensitivity to market conditions

Factor 4: Multilearning Learning by doing in two dimensions

– Across organization

– Across specialty

Enhanced learning opportunities

– 15% of time devoted to “dreams” – 3M

– Peer pressure to study

– Send team to Europe to look around – Honda

– Bring in top academics and consultants – HP

Everyone learns multiple skills

Factor 5: Subtle Control Management establishes checkpoints

– Prevents instability, ambiguity, and tension from turning into chaos

Emphasis on self-control, control by peer pressure, control by love = “subtle control”

Management responsible for:– Selecting team members for balanced team

– Creating an open working environment

– Encouraging engineers to go out in the field

– Establishing rewards based on group performance

– Tolerating and anticipating mistakes

– Encouraging suppliers to become self-organizing

Factor 6: Organizational Transfer of Learning

Transfer knowledge outside group

– Scatter successful team to new projects

– Institutionalize practice (monthly demos at IDX)

Consciously pursue unlearning

– Next generation must be 40% better

– Cut product cycle by 80%

– Scrap old parts, processes, tools

Challenges and Opportunities Winding the Rubber Band Principle: Broad mandate and

demanding goals create tension. Anti-Waterfall Principle: Operational decisions are made

incrementally. Strategic decisions delayed to last moment. Push/Pull Principle: Differentiation in concept phase,

integration dominates in implementation phase Spread the Wealth Principle: Non-experts take on new

tasks. Cuckoo Principle: Successful SCRUMs become company

models (or they can get crushed because they are different). Control Anti-Pattern: Seniority based companies have

difficult time.

– But in times of desperation, SCRUMs are easily created.

Spiral Methodology

Barry Boehm introduced the Spiral Methodology to "fix" problems with the Waterfall Methodology. This is the most commonly used variant of the Waterfall today.

The Spiral methodology "peels the onion", progressing through "layers" of the development process. A prototype lets users determine if the project is on track, should be sent back to prior phases, or should be ended. However, the phases and phase processes are still linear.

Boehm, B.W. A Spiral Model of Software Development and Enhancement. Proceedings of an International Workshop on Software Process and Software Environments, Coto de Caza, Trabuco Canyon, California, March 27-29, 1985. Boehm, Barry. A Spiral Model of Software Development and Enhancement. ACM SIGSOFT Software Engineering Notes, August 1986. Boehm, Barry. A Spiral Model of Software Development and Enhancement. IEEE Computer, vol.21, #5, May 1988, pp 61-72.

Iterative Methology

The Iterative methodology improves on the Spiral methodology.

Each iteration consists of all of the standard Waterfall phases, but each iteration only addresses one subsystem. Further iterations can add resources to the project while ramping up the speed of delivery.

Improves cost control, reduces risk, ensures delivery of (sub)systems, and improves overall flexibility.

Still assumes that the underlying development processes are defined and linear.

SCRUM Methodology

The first and last phases (Planning and Closure) consist of defined processes.

The Sprint phase is an empirical process. It is treated as a black box that requires external controls.

Sprints are nonlinear and flexible. Sprints are used to evolve the final product.

The project is open to the environment until the Closure phase. The deliverable can be changed at any time.

The deliverable is determined during the project based on the environment.

Methodology Comparison

Risk with Current Methodologies

Any methodology is better than nothing.

Current approaches rests on the fallacy that the development processes are defined, predictable processes.

They lack flexibility needed to cope with the unpredictable results and respond to a complex environment.

Schwaber, Ken. SCRUM Development Process. Business Object Design and Implementation (Eds. Jeff Sutherland et al.). London: Springer-Verlag, 1997.

http://www.tiac.net/users/jsuth/oopsla/schwapub.pdf

SCRUM Lowers Risk

Development teams need to operate adaptively within a complex environment using imprecise processes.

SCRUM can accelerate closure by inducing the phenomenon known as "punctuated equilibrium" seen in the evolution of biological species.Levy, Steven. Artificial Life: A Report from the Frontier Where Computers Meet Biology. Vintage Books, 1993.

Lewin, Roger. Complexity: Life at the Edge of Chaos. Collier Books, 1994.




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