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First, there are several tiers of mutability in requirements. These tiers define typical levels of change context of the problem, the problem itself and the forces that select a solution to the problem.
- Natural Laws (i.e., Gravity, Natural Selection). As well as metaphysical "laws" (i.e., reality). These don't change much. Sometimes we encapsulate this information with static final constants so we can use names to identify the constants. PI, E, seconds_per_minute, etc.
- Legal Context (both statutory law and case law), as well as standards and procedures with the effect of law (i.e. GAAP). Most software products are implicitly constrained, and the constraints are so fundamental as to be immutable. They aren't design constraints, per se, they are constraints on the context space for the basic description of the problem. Like air, these are hard to see, and their effects are usually noted indirectly.
- Industry. That is to say, industry practices and procedures which are prevalent, and required before we can be called a business in a particular industry. Practices and procedures that cannot be ignored without severe, business-limiting consequences. These are more flexible than laws, but as pervasive and almost as implicit. Some software will call out industry-specific features. Health-care packages, banking packages, etc., are explicitly tailored to an industry context.
- Company. Constraints imposed by the organization of the company itself. The structure of ownership, subsidiaries, stock-holders, directors, trustees, etc. Often, this is reflected in the accounting, HR and Finance systems. The chart of accounts is the backbone of these constraints. These constraints are often canonized in customized software to create unique value based on the company's organization, or in spite of it.
- Line of Business. Line of business changes stem from special considerations for subsets of vendors, customers, or products. Sometimes it is a combination of company organization and line of business considerations, making the relationship even more obscure. Often, these are identified as special cases in software. In many cases, the fact that these are special, abnormal cases is lost, and the "normal" case is hard to isolate from all the special cases. Since these are things change, they often become opaque mysteries.
- Operational Bugs and Workarounds. Some procedures or software are actually fixes for problems introduced in other software. These are the most ephemeral of constraints. The root cause is obscure, the need for the fix is hidden, the problem is enigmatic.
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 problem X, or business rules that apply to problem 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 in each of these tiers, we can then 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 that 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 ways to implement unique features of these lower tiers: the maintenance costs of changing a packaged solution are astronomical. Internally developed solutions that require extensive development, installation and configuration aren't appropriate either.
As we move to the lower and less constrained tiers, scripted solutions using tools like Python are most appropriate. These support flexible adaptation of business processes.
Data lasts forever, therefore, the 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 likely changes to requirements (the problem) as well as the data and processing aspects of the solution. With some care, this will direct the selection of solutions.
It's important to keep mutability analysis in focus. Some folks are members of the Hand-Wringers School of Design, and consider every mutation scenario as equally likely. This is usually silly and unproductive, since their design work proceeds at a glacial pace while they overconsider the effects of fundamental changes to company, the industry, the legal context and the very nature of reality itself.
Here's my favorite quote from a member of the HWSoD: "We don't know what we don't know."
This was used to derail a conversation on security in a small web application. Managers who don't know the technology very well are panicked by statements like this. My response was that we actually do know the relevant threat scenarios, just read the OWASP vulnerabilities. Yes, some new threat may show up. No, we don't need to derail work to counter threats that do not yet exist.
The trick with mutability analysis is to do the following.
1. Time-box the work. Get something done. Make progress. A working design that is less than absolute perfection is better than no design at all. Hand-wringing over vanishingly unlikely futures is time wasted. Wasted. Create value as quickly as possible.
2. Work up from the bottom. Consider the tiers most likely to change first. Workarounds are likely to change. Features of the line of business might change. Company changes only matter if you've been specifically told the company is buying or for sale. Beyond that, it's irrelevant for the most part. ("But my software will change the industry landscape." No it won't. But if it is really novel, then delivery soon matters more than flexibility. If the landscape changes, you'll have to fundamentally rewrite it anyway.)
3. Name Names. Vague hand-waving mutation scenarios are useless. You must identify specific changes, and who will cause that change. Name the manager, customer, owner, stakeholder, executive, standard committee member, legislator or diety who will impose the change. If you can't name names, you don't really have a change scenario, you have hand-wringing. Stop worry. Get something to work.
But What If I Do Something Wrong?
What? Is it correct? Is it designed to make optimal use of resources? Can you prove it's correct, or do you have unit tests to demonstrate that it's likely to be correct? Can you prove it's optimal? Move on. Maintainability and Adaptability are nice-to-have, not central.
Getting something to work comes first. When confronted with alternative competing, correct, optimal designs, adaptability and maintainability are a way to choose between them.
Data (computing) Software
Published at DZone with permission of Steven Lott, DZone MVB. See the original article here.
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