|category:||War Stories and Advice|
First, there are several tiers of mutability in requirements in general. These tiers define typical levels of change context, problem and forces that select a solution.
Of these, tiers 1 to 3 are modeled in the very nature of the problem, context and solution. They aren’t modeled explicitly as constraints on X, or business rules that apply to X, they are modeled as X itself. These things are so hard to change that they are embodied in packaged applications, from third parties, that don’t create unique business value, but permit engaging in business to begin with.
Layers 4 to 6, however, might involve software constraints, explicitly packaged to make it clear. Mostly, these are procedural steps required to either expose or conceal special cases. Once in a while these become actual limitations on the domain of allowed data values.
After considering changes to the problem, we also have to consider changes to the solution. The mutation of the implementation can be decomposed into procedural mutation and data model mutation. The Zachman Framework gives us the hint the communication, people and motivation may also change. Often these changes are manifested through procedural or data changes.
Procedural mutation means programming changes. This implies that flexible software is required to respond to business changes, customer/vendor/product changes, and evolving workarounds for other IT bugs. Packaged solutions aren’t appropriate, the maintenance costs are astronomical. Internally developed solutions that require extensive development, installation and configuration aren’t appropriate either. Scripted solutions using tools like Python and Perl are most appropriate to support flexible adaptation of business processes.
Data model mutations fall into two deeper categories: structural and non-structural.
When data values are keys (natural, primary, surrogate or foreign) they generally must satisfy integrity constraints (they must exist, or must not exist, or are mandatory or occur 0..m times). These are structural. The data is uninterpretable, incomplete and broken without them. When these change, it is a either a profound change to the business or a long-standing bug in the data model. Either way the fix is expensive. These have to be considered carefully and understood fully.
When data values are non-key values, the constraints must be free to evolve. The semantics of non-key data fields are rarely fixed by any formalism. Changes to the semantics are rampant, and sometimes imposed by the users without resorting to software change. In the face of such change, the constraints must be chosen wisely.
“Yes, it says its the number of days overdue, but it’s really the deposit amount in pennies. They’re both numbers, after all.”
Mutability Analysis, then, seeks to characterize expected changes to requirements (the problem) as weel as the data and processing aspects of the solution. With some care, this will direct the selection of solutions.