Software does not wear out, but the world in which it runs evolves. Time and money is lost when, after implementing a feature in software, customer feedback is that the feature is not needed.
How do Waterfall and Agile implementation processes compare in the number of unneeded feature/requirements that they implement?
In a Waterfall process, a list of requirements is created and then implemented. The identity of ‘dead’ requirements is not known until customers start using the software, which is not until it is released at the end of development.
In an Agile process, a list of requirements is used to create a Minimal Viable Product, which is released to customers. An iterative development processes, driven by customer feedback, implements requirements, and makes frequent releases to customers, which reduces the likelihood of implementing known to be ‘dead’ requirements. Previously implemented requirements may be discovered to have become ‘dead’.
An analysis of the number of ‘dead’ requirements implemented by the two approaches appears at the end of this post.
The plot below shows the number of ‘dead’ requirements implemented in a project lasting a given number of working days (blue/red) and the difference between them (green), assuming that one requirement is implemented per working day, with the discovery after 100 working days that a given fraction of implemented requirements are not needed, and the number of requirements in the MVP is assumed to be small (fractions 0.5, 0.1, and 0.05 shown; code):
The values calculated using one requirement implemented per day scales linearly with requirements implemented per day.
By implementing fewer ‘dead’ requirements, an Agile project will finish earlier (assuming it only implements all the needed requirements of a Waterfall approach, and some subset of the ‘dead’ requirements). However, unless a project is long-running, or has a high requirements’ ‘death’ rate, the difference may not be compelling.
I’m not aware of any data on rate of discovery of ‘dead’ implemented requirements (there is some on rate of discovery of new requirements); as always, pointers to data most welcome.
The Waterfall projects I am familiar with, plus those where data is available, include some amount of requirement discovery during implementation. This has the potential to reduce the number of ‘dead’ implemented requirements, but who knows by how much.
As the size of Minimal Viable Product increases to become a significant fraction of the final software system, the number of fraction of ‘dead’ requirements will approach that of the Waterfall approach.
There are other factors that favor either Waterfall or Agile, which are left to be discussed in future posts.
The following is an analysis of Waterfall/Agile requirements’ implementation.
is the fraction of requirements per day that remain relevant to customers. This value is likely to be very close to one, e.g., .
requirements implemented per working day.
The implementation of requirements takes days, and the number of implemented ‘dead’ requirements is (assuming that the no ‘dead’ requirements were present at the end of the requirements gathering phase):
As effectively all implemented requirements are ‘dead’.
The number of implemented ‘live’ requirements on day is given by:
with the initial condition that the number of implemented requirements at the start of the first day of iterative development is the number of requirements implemented in the Minimum Viable Product, i.e., .
Solving this difference equation gives the number of ‘live’ requirements on day :
as , approaches to its maximum value of
Subtracting the number of ‘live’ requirements from the total number of requirements implemented gives:
as effectively all implemented requirements are ‘dead’, because the number of ‘live’ requirements cannot exceed a known maximum.