An introduction to the 'evolutionary delivery' method

1. An introduction to the 'evolutionary delivery' method Principles of Software Engineering Management Chapter 7 By Tom Gilb (1988)

2. “Current” Models (1988) • most models of software engineering are based on the "waterfall model" for delivery • single delivery date • prototypes are "throw away“ (no reworking) • analysis and design before code and test

3. “Current” Models (1988)

4. The Evolutionary Method • Divide and conquer • deliver something to a real end-user • measure added value to user • adjust design and objectives • An “eternal cycle” of modifying an existing system rather than building a new one • Reduces risk • simpler to modify • easier to test

5. Revolutionary/Evolutionary Delivery Revolutionary Model Evolutionary Model

6. The Evolutionary Method Set Objectives Rough Evolutionary Plans Engineer the Step Construct the Planned Step Deliver to Real Users Analyze Results

7. Concepts of the evolutionary method • Planning for multiple objectives • Early, frequent iteration • Complete analysis, design, build and test at each step • User orientation • System approach instead of algorithm orientation • Open-ended basic system architecture • Result orientation instead of development process orientation

8. 1. Planning for multiple objectives • Conventional Software Development • function oriented • control of quality and resources is less important • lack of knowledge about defining and measuring "usability" or "maintainability” • Evolutionary Delivery • iterations towards clear, measurable, multidimensional objectives • functional, quality and resource objectives are defined • projects are more related to real world needs of user

9.  2. Early, frequent iteration • Conventional Software Development • Delivery of first practical/useful results is at least one year ahead • Cycle before first delivery could be divided into many (10-100) smaller and earlier steps • Phased planning – not exactly evolutionary • Evolutionary Delivery • Focus on accomplishing something useful with minimum resources, not on accomplishing as much as possible within a (budget/deadline/etc) constraint, like in phased planning • Select the right parts for early implementation- parts with high user-value to development-cost ratio • financial user value • high-risk parts • parts that convince users that program is useful

10. 3. Complete analysis, design, build and test at each step • Conventional Software Development • Waste time in • requirement analysis • detailed design • full coding and testing phases • Assume that detailed analyzing before construction prevents construction errors • Difficult to do accurately for big software projects: • too many unknowns • too many dynamic changes • complex set of interrelations • Negative feedback at delivery  many resources were committed to the wrong solutions

11. 3. Complete analysis, design, build and test at each step • Evolutionary Delivery • set initial objectives, but be prepared to modify them • set measurable objectives for each next delivery phase • could also be modified if necessary • compromise and tradeoff: not all objectives are fully met • design, build and test immediate technical solution • deliver solution, get feedback and use it to modify: • immediate design and architectural ideas • short/long-term objectives • gives us early warning signals for problems with software • start with ‘open ended’ architecture (easy to modify and adapt)

12. The Evolutionary Method Set Objectives Rough Evolutionary Plans Engineer the Step Construct the Planned Step Deliver to Real Users Analyze Results

13. 4. User Orientation • Conventional Software Development • orientation towards machine/algorithm/deadline, not user • usually developers don’t see real end users using software • even if developers were more user-oriented, by the time they understand users’ needs it might be too late • Evolutionary Delivery • developers must listen to user reactions early and often • be mentally, economically and technically prepared to listen to users • user values are dynamic • alter as users get experience • parts that are selected for development may change

14. 5. System approach instead of algorithm orientation • Conventional Software Development • more focus on algorithm and programming language • little focus on data engineering, documentation, marketing • Evolutionary Delivery • architecture coordination of design process as a whole • a method that is suited for any creative process (not merely software engineering)

15. 6. Open-ended basic system architecture • Evolutionary Delivery • open architectures are essential, because they enable us to avoid problems with software maintenance • principle attributes of a system should allow it to survive and succeed with changes over time: • maintainability • portability • extendibility • a good software engineer should constantly keep up with available design technologies that lead to more adaptable systems

16. 7. Result orientation instead of development process orientation • Conventional Software Development • The process seems more important than the result, because there are no clear objectives on which to focus effort • Evolutionary Delivery • sets clear objectives regarding quality and resources • constant measure of progress towards the goals

17. 7. Result orientation instead of development process orientation

18. Not Knowing, Chess and Driving • It is fine not to know everything at any given time of the development process • evolutionary delivery is like chess: have a strategy, but respond to immediate realities (opponent's last move) • “there is only one move that really counts: the next one” • evolutionary delivery is like driving a car: we must plan our driving, but we should not necessarily drive the way we planned

19. Small is Beautiful • The problem of result control: • the outcome of implementing a software project is difficult to predict • unexpected results affect the project attributes (usually "in the wrong direction“) • Solution: keeping implementation steps small and simple • Like a scientific experiment: keep constant all factors but one, vary just one factor, and test its impact • It is easier to deal with the effect of one small increment of the solution than it is to understand the impact of the entire solution

20. Characteristics of Evolutionary Steps • traditional phased projects are created by making phases as large as could be fit within a given budget • with evolutionary delivery, we create smaller phases that achieve maximum value with minimum cost

21. Characteristics of Evolutionary Steps • The system only gets some kind of reality after the delivery of the first sub-step • After the initial delivery stage we analyze: • how long did it take • what unexpected resources did it consume • is the design on the right path, or do we have to change concepts

22. Characteristics of Evolutionary Steps

23. Planning Evolutionary Steps • It is not always possible to pre-plan the best set of steps, since it is not possible to know which user requirements will change • it is probably best to ask at each step, "what is now the next best step?“ • feedback and real-world data that is provided by each step should be used for planning subsequent steps

24. Project Estimates and Evolutionary Design • Evolutionary design leads to dynamic planning, since estimates are constantly being improved • plans made with evolutionary method are more realistic than plans that are made in detail before the beginning of the process • real results will correspond closely with the latest adjusted estimates • Planning is like a model of the real world at a particular point in time – idealized and simplified • Evolutionary planning closes the gap between theory and reality • planners are more aware of effects of the plan on the budget, resources and satisfactions of clients

25. Objections to Evolutionary Delivery • almost any project was found to be possible for division into interesting steps • if you think a project is too small for division, you might be under-estimating its size • sometimes evolutionary design is done by initially improving an existing system, then turning it into a new system • If a certain design is wrong for evolutionary delivery, maybe creating a totally different design architecture is a better idea • our system can't be divided into smaller steps

26. Objections to Evolutionary Delivery • the current estimation of delivery date is probably optimistic… • evolutionary design allows to deliver the most critical results much earlier • Probably, the entire long-range solution will be delivered earlier than with other delivery methods • we are in a hurry

27. Objections to Evolutionary Delivery • people assume that the management won't like the fact that long term planning and cost estimations are initially done only roughly • management might prefer it, because they don’t commit resources to a doubtful result, or put faith in long-term results • if we fail, we lose little, if we succeed we make a big progress for the organization • early phase deliveries allow payback from the project shortly after it starts • management won't like it

28. Objections to Evolutionary Delivery • then they are not good designers, hire others instead! • or, train your designers to deliver evolutionary designs • our designers can't make evolutionary plans

29. Objections to Evolutionary Delivery THEN IT SHOULD BE! • it is not the traditional way to design and plan in our company

30. Objections to Evolutionary Delivery • true only for systems with a 'closed end' architecture, that are hard to modify • an evolutionary architecture presupposes that many changes will be made to the design during the process, thus allows modifications to be done much more easily • problems of inflexible design usually show up in the early steps of the process  can be solved early on • The extra effort of moving from step to step costs more than doing it all at once

31. My Personal Objections • constant cycles of changes in the system are difficult for the users • For users, the revolutionary method has many advantages • getting valuable feedback from real end-users is not easy • at the end of the project might leave “corners”