By: David J. Anderson
Abstract: With Agile Management [Anderson 2003] I introduced Cumulative Flow Diagrams as a better replacement for "Burn UP Charts". This article explains why.
Agile software development methods such as Scrum and
Feature Driven Development manage and report project progress in a very
different manner than traditional critical path project management. Scrum
started with the use of a "burn down" chart which plotted the estimated number
of hours remaining to complete the Sprint backlog. More recently "burn up"
charts have become popular. These plot the number of completed Stories, Tasks or
Features on the project with a projected completion target. It is possible to
extrapolate the plot with a trend line to estimate the completion date of a
The concept of a "burn up" chart had been used in
Feature Driven Development since its inception in the late 1990s. It's called a
Feature Complete Graph as shown in Figure 1.
Figure 1. FDD Feature Complete Graph
However, "burn up" charts aren't really sufficient for
managing a project. They have no concept of work-in-progress (WIP) and
simulating the anticipated end date is problematic. With Agile Management
[Anderson 2003] I introduced Cumulative Flow Diagrams as a better replacement.
This article explains why.
The completion of Features tends to follow an S-Curve
model, as shown in Figure 2. The S-curve effect makes it difficult to predict
the end-date of a project from a single plot of Features complete.
Figure 2. S-Curve for Features Complete
In order to communicate, a fuller picture of a project's
health, I found it necessary to supplement a Feature Complete Graph with a
report of the percentage complete and an accompanying graph. A percentage
complete plot for FDD gives credit to Features which are in-progress. Each of
the six milestones for a Feature is credited with an earned-value percentage,
as shown in Figure 3.
Figure 3. Feature Milestones and earned-value percentages
So Feature Complete graphs fail to communicate
work-in-progress but the Percentage Complete figure and graph suffer from
"false reporting". Why? Because any agile developer will tell you that they
only value finished working software. So there is no value in partially
complete code. This result is compatible with the Theory of Constraints
management accounting method, Throughput Accounting [Corbett 1997]. Value
should only be recognized on delivery.
Hence, it was necessary to stop reporting percentage
complete altogether. Only report the true earned value - Features complete - or
find a better way to communicate the work-in-progress, i.e. a method which did
not report the value of WIP but did communicate that work was progressing even
when Features were not being completed every day.
If it is wrong to communicate earned value from partially
complete WIP, then we should ignore it? No, I don't believe so! Rather, we
should care about it deeply. Why?
Work-in-progress can be thought of as inventory. In this
case, it is knowledge work inventory - ideas for valuable working software,
captured at some stage in a transformational process which takes it through
transformations such as analysis, design, test plan, code, unit test and code
inspection (depending on the method you are using). These ideas are developed
from the work of Donald Reinertsen but to understand that, we must first
understand the contribution of Marvin Patterson.
With Accelerating Innovation  Marvin Patterson
introduced a concept for modeling the design process. He asked us to envisage
that design was a process of information discovery. Before a design is started
there is little or no information, perhaps only a vague thought. As the design
emerges there is gradually more and more information and less and less
uncertainty until the design is complete.
Mary Poppendieck  suggested that software
development can be understood with the same model. In other words, all software
development is a "design problem". This would allow us to model the flow of
value through a software engineering system as the gradual reduction of
uncertainty and the discovery of more and more detailed information until
working code which passes appropriate quality control tests, is produced.
Writing in Managing the Design Factory
 Donald Reinertsen developed the ideas of Patterson a little further by
introducing two concepts. The first observation was that design-in-process
inventory could be tracked using Cumulative Flow Diagrams, as shown in Figure
4. CFDs were already in use in Lean Production to track the flow of value
through a factory. The second and perhaps the most valuable insight was that
the value of design information depreciates over time. There are several
reasons for this. The main one is that information need only be created once,
as the cost of replicating it is near zero. If design is information then the
time it takes a competitor to duplicate the design, is the time in which the design
(and its associated information) has a differentiating value. Design
information is also perishable because of possible changes in the marketplace -
fashions change and so do laws, regulations, materials, supply components,
distribution networks and business models. To have real value a design must be
appropriate for its time and it must come to market within its window of
appropriateness. If software is design then the same must be true of it. The
requirements for a software program must be perishable. Hence, the faster the
requirements can be realized as working code and brought to market, the more
value will be delivered. Reinertsen's and Poppendieck's observations tie
software engineering firmly to the principles of Lean and the lead time for turning
an idea into working software must be a critical to the financial success of
any software activity.
Figure 4. An idealized Cumulative Flow Diagram for an FDD project
Little's Law states that a queue of material can be
analyzed in two ways: as inventory; or lead time. Simply put the size of the
inventory is directly proportional to the lead time for processing that
inventory. Hence, the size of work-in-process inventory matters because in
agile development we want to complete software in short cycles and deliver
value as often as possible. Figure 5 shows how to read the WIP inventory and
Lead Times from a CFD.
Figure 5. Reading
WIP and Lead Time from a CFD for Day 40 of a project
Figure 5 also demonstrates how batch size
and batch transfers affect the cumulative flow plot. The batch transfers can be
clearly seen from the jaggedness of the plot. With larger batch sizes, there is
more WIP and longer lead times. With smaller batch sizes as in Figure 6, WIP is
reduced and lead time falls accordingly. Note the smoothness of the plot in
Figure 6. This is from a real FDD project with Chief Programmer Work Packages
(small batches of Features) which were completed in less than 2 weeks. The lead
times can be clearly read from the diagram.
It is easy to see from this that CFDs can be used to
tell at a glance, the size of iterations and the type of method being used for
Figure 6. CFD showing lead time fall as a result of reduced WIP
Reinertsen describes WIP as a leading metric. What he
means is that WIP can predict in advance the lead time and delivery date. It
can therefore be used to correct problems before they become too serious. If we
waited to measure lead time or delivery date, there may be greater problems
stored up. I once had a project which was reporting 53% complete but only 4
Features were completed. A CFD plot of this project would have alerted me, as the
manager much earlier. I will discuss how to monitor the health of projects
using CFDs in a future Coad Letter.
Cumulative Flow Diagrams provide a method for tracking
progress of agile projects in a "burn up" fashion. Because they plot both the total
scope and the progress of individual Features / Stories / Tasks / Functions /
Use Cases they communicate absolute progress whilst visually providing a
proportional message of total completeness. CFDs also offer us a simple method
of tracking work-in-progress and visually analyzing the trend in lead time for
delivery of working code. They provide a leading metric which allows teams and
managers to react early to growing problems and provide transparency into the
whole lifecycle. Tracking a project with a CFD is a key element in moving to a
Lean system for software development.
David J. Anderson is the author of the recent
book, "Agile Management for Software Engineering - Applying the Theory of
Constraints for Business Results" published in Peter Coad's series by Prentice
Hall PTR in September 2003. He is Principal Consultant with VA Systems
Professional Services. David was one of the team which created the popular
agile development method, Feature Driven Development. He has introduced FDD at
two Fortune 100 companies Sprint (a telecommunications operator in the United
States) and Motorola. He writes the regular Agile Management
column at the Borland Developer Network website and publishes the his weblog at
agilemanagement.net. He holds a
degree in Computer Science and Electronics from the University of Strathclyde.
[Anderson 2003] Anderson,
David J., Agile Management for Software Engineering - Applying the Theory of
Constraints for Business Results, Prentice Hall, Upper Saddle River NJ, 2003
[Corbett 1997] Corbett,
Thomas, Throughput Accounting, North River Press, Great Barrington MA, 1997
[Patterson 1993] Patterson, Marvin, Accelerating
Innovation, Van Nostrand Reinhold, New York NY, 1993
[Poppendieck 2003] Poppendieck, Mary and Tom Poppendieck, Lean Software Development -
an agile toolkit, Addison Wesley, New York NY, 2003
[Reinertsen 1997] Reinertsen, Donald G., Managing the Design Factory - A Product Developer's
Toolkit, Free Press, New York NY, 1997
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